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<h1 id="index">Index</h1>

<div class="genindex-jumpbox">
 <a href="#A"><strong>A</strong></a>
 | <a href="#B"><strong>B</strong></a>
 | <a href="#C"><strong>C</strong></a>
 | <a href="#D"><strong>D</strong></a>
 | <a href="#E"><strong>E</strong></a>
 | <a href="#F"><strong>F</strong></a>
 | <a href="#G"><strong>G</strong></a>
 | <a href="#H"><strong>H</strong></a>
 | <a href="#I"><strong>I</strong></a>
 | <a href="#J"><strong>J</strong></a>
 | <a href="#K"><strong>K</strong></a>
 | <a href="#L"><strong>L</strong></a>
 | <a href="#M"><strong>M</strong></a>
 | <a href="#N"><strong>N</strong></a>
 | <a href="#O"><strong>O</strong></a>
 | <a href="#P"><strong>P</strong></a>
 | <a href="#R"><strong>R</strong></a>
 | <a href="#S"><strong>S</strong></a>
 | <a href="#T"><strong>T</strong></a>
 | <a href="#U"><strong>U</strong></a>
 | <a href="#V"><strong>V</strong></a>
 | <a href="#W"><strong>W</strong></a>
 | <a href="#X"><strong>X</strong></a>
 | <a href="#Z"><strong>Z</strong></a>
 
</div>
<h2 id="A">A</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="prml.linear.html#prml.linear.variational_linear_regression.VariationalLinearRegression.a">a (prml.linear.variational_linear_regression.VariationalLinearRegression attribute)</a>

      <ul>
        <li><a href="prml.linear.html#prml.linear.VariationalLinearRegression.a">(prml.linear.VariationalLinearRegression attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.Gamma.a">(prml.rv.Gamma attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.gamma.Gamma.a">(prml.rv.gamma.Gamma attribute)</a>
</li>
      </ul></li>
      <li><a href="prml.nn.optimizer.html#prml.nn.optimizer.ada_delta.AdaDelta">AdaDelta (class in prml.nn.optimizer.ada_delta)</a>
</li>
      <li><a href="prml.nn.optimizer.html#prml.nn.optimizer.ada_grad.AdaGrad">AdaGrad (class in prml.nn.optimizer.ada_grad)</a>
</li>
      <li><a href="prml.nn.optimizer.html#prml.nn.optimizer.adam.Adam">Adam (class in prml.nn.optimizer.adam)</a>
</li>
      <li><a href="prml.nn.math.html#prml.nn.math.add.Add">Add (class in prml.nn.math.add)</a>
</li>
      <li><a href="prml.nn.math.html#prml.nn.math.add.add">add() (in module prml.nn.math.add)</a>
</li>
      <li><a href="prml.bayesnet.html#prml.bayesnet.discrete.DiscreteVariable.add_child">add_child() (prml.bayesnet.discrete.DiscreteVariable method)</a>

      <ul>
        <li><a href="prml.bayesnet.html#prml.bayesnet.DiscreteVariable.add_child">(prml.bayesnet.DiscreteVariable method)</a>
</li>
      </ul></li>
      <li><a href="prml.bayesnet.html#prml.bayesnet.discrete.DiscreteVariable.add_parent">add_parent() (prml.bayesnet.discrete.DiscreteVariable method)</a>

      <ul>
        <li><a href="prml.bayesnet.html#prml.bayesnet.DiscreteVariable.add_parent">(prml.bayesnet.DiscreteVariable method)</a>
</li>
      </ul></li>
      <li><a href="prml.nn.math.html#prml.nn.math.add.AddBias">AddBias (class in prml.nn.math.add)</a>
</li>
      <li><a href="prml.nn.math.html#prml.nn.math.add.AddScalar">AddScalar (class in prml.nn.math.add)</a>
</li>
  </ul></td>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="prml.kernel.html#prml.kernel.relevance_vector_classifier.RelevanceVectorClassifier.alpha">alpha (prml.kernel.relevance_vector_classifier.RelevanceVectorClassifier attribute)</a>

      <ul>
        <li><a href="prml.kernel.html#prml.kernel.RelevanceVectorClassifier.alpha">(prml.kernel.RelevanceVectorClassifier attribute)</a>
</li>
        <li><a href="prml.kernel.html#prml.kernel.RelevanceVectorRegressor.alpha">(prml.kernel.RelevanceVectorRegressor attribute)</a>
</li>
        <li><a href="prml.kernel.html#prml.kernel.relevance_vector_regressor.RelevanceVectorRegressor.alpha">(prml.kernel.relevance_vector_regressor.RelevanceVectorRegressor attribute)</a>
</li>
        <li><a href="prml.linear.html#prml.linear.VariationalLogisticRegression.alpha">(prml.linear.VariationalLogisticRegression attribute)</a>
</li>
        <li><a href="prml.linear.html#prml.linear.variational_logistic_regression.VariationalLogisticRegression.alpha">(prml.linear.variational_logistic_regression.VariationalLogisticRegression attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.Dirichlet.alpha">(prml.rv.Dirichlet attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.VariationalGaussianMixture.alpha">(prml.rv.VariationalGaussianMixture attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.dirichlet.Dirichlet.alpha">(prml.rv.dirichlet.Dirichlet attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.variational_gaussian_mixture.VariationalGaussianMixture.alpha">(prml.rv.variational_gaussian_mixture.VariationalGaussianMixture attribute)</a>
</li>
      </ul></li>
      <li><a href="prml.nn.array.html#prml.nn.array.array.Array">Array (class in prml.nn.array.array)</a>
</li>
      <li><a href="prml.nn.array.html#prml.nn.array.array.array">array() (in module prml.nn.array.array)</a>
</li>
      <li><a href="prml.nn.array.html#prml.nn.array.array.asarray">asarray() (in module prml.nn.array.array)</a>
</li>
      <li><a href="prml.dimreduction.html#prml.dimreduction.Autoencoder">Autoencoder (class in prml.dimreduction)</a>

      <ul>
        <li><a href="prml.dimreduction.html#prml.dimreduction.autoencoder.Autoencoder">(class in prml.dimreduction.autoencoder)</a>
</li>
      </ul></li>
  </ul></td>
</tr></table>

<h2 id="B">B</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="prml.linear.html#prml.linear.variational_linear_regression.VariationalLinearRegression.b">b (prml.linear.variational_linear_regression.VariationalLinearRegression attribute)</a>

      <ul>
        <li><a href="prml.linear.html#prml.linear.VariationalLinearRegression.b">(prml.linear.VariationalLinearRegression attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.Gamma.b">(prml.rv.Gamma attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.gamma.Gamma.b">(prml.rv.gamma.Gamma attribute)</a>
</li>
      </ul></li>
      <li><a href="prml.nn.html#prml.nn.queue.BackPropQueue">BackPropQueue (class in prml.nn.queue)</a>
</li>
      <li><a href="prml.nn.array.html#prml.nn.array.array.Array.backward">backward() (prml.nn.array.array.Array method)</a>

      <ul>
        <li><a href="prml.nn.html#prml.nn.function.Function.backward">(prml.nn.function.Function method)</a>
</li>
        <li><a href="prml.nn.math.html#prml.nn.math.product.Product.backward">(prml.nn.math.product.Product method)</a>
</li>
      </ul></li>
      <li><a href="prml.nn.normalization.html#prml.nn.normalization.batch_normalization.BatchNormalization">BatchNormalization (class in prml.nn.normalization.batch_normalization)</a>
</li>
      <li><a href="prml.nn.normalization.html#prml.nn.normalization.batch_normalization.BatchNormalizationFunction">BatchNormalizationFunction (class in prml.nn.normalization.batch_normalization)</a>
</li>
      <li><a href="prml.linear.html#prml.linear.BayesianLogisticRegression">BayesianLogisticRegression (class in prml.linear)</a>

      <ul>
        <li><a href="prml.linear.html#prml.linear.bayesian_logistic_regression.BayesianLogisticRegression">(class in prml.linear.bayesian_logistic_regression)</a>
</li>
      </ul></li>
      <li><a href="prml.dimreduction.html#prml.dimreduction.BayesianPCA">BayesianPCA (class in prml.dimreduction)</a>

      <ul>
        <li><a href="prml.dimreduction.html#prml.dimreduction.bayesian_pca.BayesianPCA">(class in prml.dimreduction.bayesian_pca)</a>
</li>
      </ul></li>
  </ul></td>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="prml.linear.html#prml.linear.BayesianRegression">BayesianRegression (class in prml.linear)</a>

      <ul>
        <li><a href="prml.linear.html#prml.linear.bayesian_regression.BayesianRegression">(class in prml.linear.bayesian_regression)</a>
</li>
      </ul></li>
      <li><a href="prml.nn.distribution.html#prml.nn.distribution.bernoulli.Bernoulli">Bernoulli (class in prml.nn.distribution.bernoulli)</a>

      <ul>
        <li><a href="prml.rv.html#prml.rv.Bernoulli">(class in prml.rv)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.bernoulli.Bernoulli">(class in prml.rv.bernoulli)</a>
</li>
      </ul></li>
      <li><a href="prml.rv.html#prml.rv.BernoulliMixture">BernoulliMixture (class in prml.rv)</a>

      <ul>
        <li><a href="prml.rv.html#prml.rv.bernoulli_mixture.BernoulliMixture">(class in prml.rv.bernoulli_mixture)</a>
</li>
      </ul></li>
      <li><a href="prml.rv.html#prml.rv.Beta">Beta (class in prml.rv)</a>

      <ul>
        <li><a href="prml.rv.html#prml.rv.beta.Beta">(class in prml.rv.beta)</a>
</li>
      </ul></li>
      <li><a href="prml.rv.html#prml.rv.variational_gaussian_mixture.VariationalGaussianMixture.beta">beta (prml.rv.variational_gaussian_mixture.VariationalGaussianMixture attribute)</a>

      <ul>
        <li><a href="prml.rv.html#prml.rv.VariationalGaussianMixture.beta">(prml.rv.VariationalGaussianMixture attribute)</a>
</li>
      </ul></li>
      <li><a href="prml.nn.html#prml.nn.function.broadcast">broadcast() (in module prml.nn.function)</a>
</li>
      <li><a href="prml.nn.html#prml.nn.function.broadcast_to">broadcast_to() (in module prml.nn.function)</a>
</li>
      <li><a href="prml.nn.html#prml.nn.function.BroadcastTo">BroadcastTo (class in prml.nn.function)</a>
</li>
  </ul></td>
</tr></table>

<h2 id="C">C</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="prml.dimreduction.html#prml.dimreduction.PCA.C">C (prml.dimreduction.PCA attribute)</a>

      <ul>
        <li><a href="prml.dimreduction.html#prml.dimreduction.pca.PCA.C">(prml.dimreduction.pca.PCA attribute)</a>
</li>
      </ul></li>
      <li><a href="prml.nn.distribution.html#prml.nn.distribution.categorical.Categorical">Categorical (class in prml.nn.distribution.categorical)</a>

      <ul>
        <li><a href="prml.rv.html#prml.rv.Categorical">(class in prml.rv)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.categorical.Categorical">(class in prml.rv.categorical)</a>
</li>
      </ul></li>
      <li><a href="prml.markov.html#prml.markov.CategoricalHMM">CategoricalHMM (class in prml.markov)</a>

      <ul>
        <li><a href="prml.markov.html#prml.markov.categorical_hmm.CategoricalHMM">(class in prml.markov.categorical_hmm)</a>
</li>
      </ul></li>
      <li><a href="prml.nn.distribution.html#prml.nn.distribution.categorical.CategoricalPDF">CategoricalPDF (class in prml.nn.distribution.categorical)</a>
</li>
      <li><a href="prml.dimreduction.html#prml.dimreduction.PCA.Cinv">Cinv (prml.dimreduction.PCA attribute)</a>

      <ul>
        <li><a href="prml.dimreduction.html#prml.dimreduction.pca.PCA.Cinv">(prml.dimreduction.pca.PCA attribute)</a>
</li>
      </ul></li>
      <li><a href="prml.rv.html#prml.rv.bernoulli_mixture.BernoulliMixture.classfiy_proba">classfiy_proba() (prml.rv.bernoulli_mixture.BernoulliMixture method)</a>

      <ul>
        <li><a href="prml.rv.html#prml.rv.BernoulliMixture.classfiy_proba">(prml.rv.BernoulliMixture method)</a>
</li>
      </ul></li>
      <li><a href="prml.linear.html#prml.linear.classifier.Classifier">Classifier (class in prml.linear.classifier)</a>
</li>
      <li><a href="prml.linear.html#prml.linear.fishers_linear_discriminant.FishersLinearDiscriminant.classify">classify() (prml.linear.fishers_linear_discriminant.FishersLinearDiscriminant method)</a>

      <ul>
        <li><a href="prml.linear.html#prml.linear.FishersLinearDiscriminant.classify">(prml.linear.FishersLinearDiscriminant method)</a>
</li>
        <li><a href="prml.linear.html#prml.linear.LeastSquaresClassifier.classify">(prml.linear.LeastSquaresClassifier method)</a>
</li>
        <li><a href="prml.linear.html#prml.linear.LogisticRegression.classify">(prml.linear.LogisticRegression method)</a>
</li>
        <li><a href="prml.linear.html#prml.linear.Perceptron.classify">(prml.linear.Perceptron method)</a>
</li>
        <li><a href="prml.linear.html#prml.linear.SoftmaxRegression.classify">(prml.linear.SoftmaxRegression method)</a>
</li>
        <li><a href="prml.linear.html#prml.linear.least_squares_classifier.LeastSquaresClassifier.classify">(prml.linear.least_squares_classifier.LeastSquaresClassifier method)</a>
</li>
        <li><a href="prml.linear.html#prml.linear.logistic_regression.LogisticRegression.classify">(prml.linear.logistic_regression.LogisticRegression method)</a>
</li>
        <li><a href="prml.linear.html#prml.linear.perceptron.Perceptron.classify">(prml.linear.perceptron.Perceptron method)</a>
</li>
        <li><a href="prml.linear.html#prml.linear.softmax_regression.SoftmaxRegression.classify">(prml.linear.softmax_regression.SoftmaxRegression method)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.BernoulliMixture.classify">(prml.rv.BernoulliMixture method)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.MultivariateGaussianMixture.classify">(prml.rv.MultivariateGaussianMixture method)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.VariationalGaussianMixture.classify">(prml.rv.VariationalGaussianMixture method)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.bernoulli_mixture.BernoulliMixture.classify">(prml.rv.bernoulli_mixture.BernoulliMixture method)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.multivariate_gaussian_mixture.MultivariateGaussianMixture.classify">(prml.rv.multivariate_gaussian_mixture.MultivariateGaussianMixture method)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.variational_gaussian_mixture.VariationalGaussianMixture.classify">(prml.rv.variational_gaussian_mixture.VariationalGaussianMixture method)</a>
</li>
      </ul></li>
  </ul></td>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="prml.rv.html#prml.rv.multivariate_gaussian_mixture.MultivariateGaussianMixture.classify_proba">classify_proba() (prml.rv.multivariate_gaussian_mixture.MultivariateGaussianMixture method)</a>

      <ul>
        <li><a href="prml.rv.html#prml.rv.MultivariateGaussianMixture.classify_proba">(prml.rv.MultivariateGaussianMixture method)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.VariationalGaussianMixture.classify_proba">(prml.rv.VariationalGaussianMixture method)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.variational_gaussian_mixture.VariationalGaussianMixture.classify_proba">(prml.rv.variational_gaussian_mixture.VariationalGaussianMixture method)</a>
</li>
      </ul></li>
      <li><a href="prml.nn.html#prml.nn.network.Network.clear">clear() (prml.nn.network.Network method)</a>
</li>
      <li><a href="prml.nn.array.html#prml.nn.array.array.Array.cleargrad">cleargrad() (prml.nn.array.array.Array method)</a>
</li>
      <li><a href="prml.rv.html#prml.rv.bernoulli_mixture.BernoulliMixture.coef">coef (prml.rv.bernoulli_mixture.BernoulliMixture attribute)</a>

      <ul>
        <li><a href="prml.rv.html#prml.rv.BernoulliMixture.coef">(prml.rv.BernoulliMixture attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.MultivariateGaussianMixture.coef">(prml.rv.MultivariateGaussianMixture attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.multivariate_gaussian_mixture.MultivariateGaussianMixture.coef">(prml.rv.multivariate_gaussian_mixture.MultivariateGaussianMixture attribute)</a>
</li>
      </ul></li>
      <li><a href="prml.bayesnet.html#prml.bayesnet.discrete.DiscreteProbability.compute_message_to">compute_message_to() (prml.bayesnet.discrete.DiscreteProbability method)</a>
</li>
      <li><a href="prml.nn.html#prml.nn.config.Config">Config (class in prml.nn.config)</a>
</li>
      <li><a href="prml.nn.image.html#prml.nn.image.convolve2d.Convolve2d">Convolve2d (class in prml.nn.image.convolve2d)</a>
</li>
      <li><a href="prml.nn.image.html#prml.nn.image.convolve2d.convolve2d">convolve2d() (in module prml.nn.image.convolve2d)</a>
</li>
      <li><a href="prml.nn.image.html#prml.nn.image.convolve2d.Convolve2dFunction">Convolve2dFunction (class in prml.nn.image.convolve2d)</a>
</li>
      <li><a href="prml.kernel.html#prml.kernel.relevance_vector_classifier.RelevanceVectorClassifier.cov">cov (prml.kernel.relevance_vector_classifier.RelevanceVectorClassifier attribute)</a>

      <ul>
        <li><a href="prml.kernel.html#prml.kernel.RelevanceVectorClassifier.cov">(prml.kernel.RelevanceVectorClassifier attribute)</a>
</li>
        <li><a href="prml.kernel.html#prml.kernel.RelevanceVectorRegressor.cov">(prml.kernel.RelevanceVectorRegressor attribute)</a>
</li>
        <li><a href="prml.kernel.html#prml.kernel.relevance_vector_regressor.RelevanceVectorRegressor.cov">(prml.kernel.relevance_vector_regressor.RelevanceVectorRegressor attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.MultivariateGaussian.cov">(prml.rv.MultivariateGaussian attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.MultivariateGaussianMixture.cov">(prml.rv.MultivariateGaussianMixture attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.multivariate_gaussian.MultivariateGaussian.cov">(prml.rv.multivariate_gaussian.MultivariateGaussian attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.multivariate_gaussian_mixture.MultivariateGaussianMixture.cov">(prml.rv.multivariate_gaussian_mixture.MultivariateGaussianMixture attribute)</a>
</li>
      </ul></li>
      <li><a href="prml.kernel.html#prml.kernel.gaussian_process_regressor.GaussianProcessRegressor.covariance">covariance (prml.kernel.gaussian_process_regressor.GaussianProcessRegressor attribute)</a>

      <ul>
        <li><a href="prml.kernel.html#prml.kernel.GaussianProcessRegressor.covariance">(prml.kernel.GaussianProcessRegressor attribute)</a>
</li>
      </ul></li>
  </ul></td>
</tr></table>

<h2 id="D">D</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="prml.preprocess.html#prml.preprocess.label_transformer.LabelTransformer.decode">decode() (prml.preprocess.label_transformer.LabelTransformer method)</a>

      <ul>
        <li><a href="prml.preprocess.html#prml.preprocess.LabelTransformer.decode">(prml.preprocess.LabelTransformer method)</a>
</li>
      </ul></li>
      <li><a href="prml.nn.image.html#prml.nn.image.deconvolve2d.Deconvolve2d">Deconvolve2d (class in prml.nn.image.deconvolve2d)</a>
</li>
      <li><a href="prml.nn.image.html#prml.nn.image.deconvolve2d.deconvolve2d">deconvolve2d() (in module prml.nn.image.deconvolve2d)</a>
</li>
      <li><a href="prml.nn.image.html#prml.nn.image.deconvolve2d.Deconvolve2dFunction">Deconvolve2dFunction (class in prml.nn.image.deconvolve2d)</a>
</li>
      <li><a href="prml.nn.html#prml.nn.queue.BackPropQueue.dequeue">dequeue() (prml.nn.queue.BackPropQueue method)</a>
</li>
      <li><a href="prml.kernel.html#prml.kernel.RBF.derivatives">derivatives() (prml.kernel.RBF method)</a>

      <ul>
        <li><a href="prml.kernel.html#prml.kernel.rbf.RBF.derivatives">(prml.kernel.rbf.RBF method)</a>
</li>
      </ul></li>
      <li><a href="prml.rv.html#prml.rv.Dirichlet">Dirichlet (class in prml.rv)</a>

      <ul>
        <li><a href="prml.rv.html#prml.rv.dirichlet.Dirichlet">(class in prml.rv.dirichlet)</a>
</li>
      </ul></li>
      <li><a href="prml.bayesnet.html#prml.bayesnet.discrete">discrete() (in module prml.bayesnet)</a>

      <ul>
        <li><a href="prml.bayesnet.html#prml.bayesnet.discrete.discrete">(in module prml.bayesnet.discrete)</a>
</li>
      </ul></li>
      <li><a href="prml.bayesnet.html#prml.bayesnet.discrete.DiscreteProbability">DiscreteProbability (class in prml.bayesnet.discrete)</a>
</li>
      <li><a href="prml.bayesnet.html#prml.bayesnet.DiscreteVariable">DiscreteVariable (class in prml.bayesnet)</a>

      <ul>
        <li><a href="prml.bayesnet.html#prml.bayesnet.discrete.DiscreteVariable">(class in prml.bayesnet.discrete)</a>
</li>
      </ul></li>
      <li><a href="prml.kernel.html#prml.kernel.support_vector_classifier.SupportVectorClassifier.distance">distance() (prml.kernel.support_vector_classifier.SupportVectorClassifier method)</a>

      <ul>
        <li><a href="prml.kernel.html#prml.kernel.SupportVectorClassifier.distance">(prml.kernel.SupportVectorClassifier method)</a>
</li>
      </ul></li>
      <li><a href="prml.nn.distribution.html#prml.nn.distribution.distribution.Distribution">Distribution (class in prml.nn.distribution.distribution)</a>
</li>
      <li><a href="prml.nn.math.html#prml.nn.math.divide.Divide">Divide (class in prml.nn.math.divide)</a>
</li>
  </ul></td>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="prml.nn.math.html#prml.nn.math.divide.divide">divide() (in module prml.nn.math.divide)</a>
</li>
      <li><a href="prml.rv.html#prml.rv.students_t.StudentsT.dof">dof (prml.rv.students_t.StudentsT attribute)</a>

      <ul>
        <li><a href="prml.rv.html#prml.rv.StudentsT.dof">(prml.rv.StudentsT attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.VariationalGaussianMixture.dof">(prml.rv.VariationalGaussianMixture attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.variational_gaussian_mixture.VariationalGaussianMixture.dof">(prml.rv.variational_gaussian_mixture.VariationalGaussianMixture attribute)</a>
</li>
      </ul></li>
      <li><a href="prml.markov.html#prml.markov.categorical_hmm.CategoricalHMM.draw">draw() (prml.markov.categorical_hmm.CategoricalHMM method)</a>

      <ul>
        <li><a href="prml.markov.html#prml.markov.CategoricalHMM.draw">(prml.markov.CategoricalHMM method)</a>
</li>
        <li><a href="prml.markov.html#prml.markov.GaussianHMM.draw">(prml.markov.GaussianHMM method)</a>
</li>
        <li><a href="prml.markov.html#prml.markov.gaussian_hmm.GaussianHMM.draw">(prml.markov.gaussian_hmm.GaussianHMM method)</a>
</li>
        <li><a href="prml.nn.distribution.html#prml.nn.distribution.distribution.Distribution.draw">(prml.nn.distribution.distribution.Distribution method)</a>
</li>
        <li><a href="prml.nn.random.html#prml.nn.random.random.RandomVariable.draw">(prml.nn.random.random.RandomVariable method)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.rv.RandomVariable.draw">(prml.rv.rv.RandomVariable method)</a>
</li>
      </ul></li>
      <li><a href="prml.nn.random.html#prml.nn.random.dropout.dropout">dropout() (in module prml.nn.random.dropout)</a>
</li>
      <li><a href="prml.nn.random.html#prml.nn.random.dropout.DropoutFunction">DropoutFunction (class in prml.nn.random.dropout)</a>
</li>
      <li><a href="prml.nn.array.html#prml.nn.array.array.Array.dtype">dtype (prml.nn.array.array.Array attribute)</a>

      <ul>
        <li><a href="prml.nn.html#prml.nn.config.Config.dtype">(prml.nn.config.Config attribute)</a>
</li>
      </ul></li>
      <li><a href="prml.markov.html#prml.markov.Kalman.Dx">Dx (prml.markov.Kalman attribute)</a>

      <ul>
        <li><a href="prml.markov.html#prml.markov.kalman.Kalman.Dx">(prml.markov.kalman.Kalman attribute)</a>
</li>
      </ul></li>
      <li><a href="prml.markov.html#prml.markov.Kalman.Dz">Dz (prml.markov.Kalman attribute)</a>

      <ul>
        <li><a href="prml.markov.html#prml.markov.kalman.Kalman.Dz">(prml.markov.kalman.Kalman attribute)</a>
</li>
      </ul></li>
  </ul></td>
</tr></table>

<h2 id="E">E</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="prml.dimreduction.html#prml.dimreduction.PCA.eigen">eigen() (prml.dimreduction.PCA method)</a>

      <ul>
        <li><a href="prml.dimreduction.html#prml.dimreduction.pca.PCA.eigen">(prml.dimreduction.pca.PCA method)</a>
</li>
      </ul></li>
      <li><a href="prml.dimreduction.html#prml.dimreduction.PCA.em">em() (prml.dimreduction.PCA method)</a>

      <ul>
        <li><a href="prml.dimreduction.html#prml.dimreduction.pca.PCA.em">(prml.dimreduction.pca.PCA method)</a>
</li>
      </ul></li>
      <li><a href="prml.linear.html#prml.linear.EmpiricalBayesRegression">EmpiricalBayesRegression (class in prml.linear)</a>

      <ul>
        <li><a href="prml.linear.html#prml.linear.emprical_bayes_regression.EmpiricalBayesRegression">(class in prml.linear.emprical_bayes_regression)</a>
</li>
      </ul></li>
      <li><a href="prml.nn.distribution.html#prml.nn.distribution.gaussian.GaussianLogPDF.enable_auto_broadcast">enable_auto_broadcast (prml.nn.distribution.gaussian.GaussianLogPDF attribute)</a>

      <ul>
        <li><a href="prml.nn.html#prml.nn.function.Function.enable_auto_broadcast">(prml.nn.function.Function attribute)</a>
</li>
        <li><a href="prml.nn.loss.html#prml.nn.loss.sigmoid_cross_entropy.SigmoidCrossEntropy.enable_auto_broadcast">(prml.nn.loss.sigmoid_cross_entropy.SigmoidCrossEntropy attribute)</a>
</li>
        <li><a href="prml.nn.math.html#prml.nn.math.add.Add.enable_auto_broadcast">(prml.nn.math.add.Add attribute)</a>
</li>
        <li><a href="prml.nn.math.html#prml.nn.math.divide.Divide.enable_auto_broadcast">(prml.nn.math.divide.Divide attribute)</a>
</li>
        <li><a href="prml.nn.math.html#prml.nn.math.multiply.Multiply.enable_auto_broadcast">(prml.nn.math.multiply.Multiply attribute)</a>
</li>
        <li><a href="prml.nn.math.html#prml.nn.math.subtract.Subtract.enable_auto_broadcast">(prml.nn.math.subtract.Subtract attribute)</a>
</li>
      </ul></li>
  </ul></td>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="prml.nn.html#prml.nn.config.Config.enable_backprop">enable_backprop (prml.nn.config.Config attribute)</a>
</li>
      <li><a href="prml.preprocess.html#prml.preprocess.label_transformer.LabelTransformer.encode">encode() (prml.preprocess.label_transformer.LabelTransformer method)</a>

      <ul>
        <li><a href="prml.preprocess.html#prml.preprocess.LabelTransformer.encode">(prml.preprocess.LabelTransformer method)</a>
</li>
      </ul></li>
      <li><a href="prml.preprocess.html#prml.preprocess.label_transformer.LabelTransformer.encoder">encoder (prml.preprocess.label_transformer.LabelTransformer attribute)</a>

      <ul>
        <li><a href="prml.preprocess.html#prml.preprocess.LabelTransformer.encoder">(prml.preprocess.LabelTransformer attribute)</a>
</li>
      </ul></li>
      <li><a href="prml.nn.html#prml.nn.queue.BackPropQueue.enqueue">enqueue() (prml.nn.queue.BackPropQueue method)</a>
</li>
      <li><a href="prml.nn.math.html#prml.nn.math.exp.Exp">Exp (class in prml.nn.math.exp)</a>
</li>
      <li><a href="prml.nn.math.html#prml.nn.math.exp.exp">exp() (in module prml.nn.math.exp)</a>
</li>
      <li><a href="prml.bayesnet.html#prml.bayesnet.discrete.DiscreteProbability.expand_dims">expand_dims() (prml.bayesnet.discrete.DiscreteProbability static method)</a>
</li>
      <li><a href="prml.markov.html#prml.markov.hmm.HiddenMarkovModel.expect">expect() (prml.markov.hmm.HiddenMarkovModel method)</a>
</li>
  </ul></td>
</tr></table>

<h2 id="F">F</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="prml.markov.html#prml.markov.Kalman.filter">filter() (prml.markov.Kalman method)</a>

      <ul>
        <li><a href="prml.markov.html#prml.markov.Particle.filter">(prml.markov.Particle method)</a>
</li>
        <li><a href="prml.markov.html#prml.markov.kalman.Kalman.filter">(prml.markov.kalman.Kalman method)</a>
</li>
        <li><a href="prml.markov.html#prml.markov.particle.Particle.filter">(prml.markov.particle.Particle method)</a>
</li>
      </ul></li>
      <li><a href="prml.markov.html#prml.markov.hmm.HiddenMarkovModel.filtering">filtering() (prml.markov.hmm.HiddenMarkovModel method)</a>

      <ul>
        <li><a href="prml.markov.html#prml.markov.Kalman.filtering">(prml.markov.Kalman method)</a>
</li>
        <li><a href="prml.markov.html#prml.markov.Particle.filtering">(prml.markov.Particle method)</a>
</li>
        <li><a href="prml.markov.html#prml.markov.kalman.Kalman.filtering">(prml.markov.kalman.Kalman method)</a>
</li>
        <li><a href="prml.markov.html#prml.markov.particle.Particle.filtering">(prml.markov.particle.Particle method)</a>
</li>
      </ul></li>
      <li><a href="prml.linear.html#prml.linear.FishersLinearDiscriminant">FishersLinearDiscriminant (class in prml.linear)</a>

      <ul>
        <li><a href="prml.linear.html#prml.linear.fishers_linear_discriminant.FishersLinearDiscriminant">(class in prml.linear.fishers_linear_discriminant)</a>
</li>
      </ul></li>
      <li><a href="prml.clustering.html#prml.clustering.k_means.KMeans.fit">fit() (prml.clustering.k_means.KMeans method)</a>

      <ul>
        <li><a href="prml.clustering.html#prml.clustering.KMeans.fit">(prml.clustering.KMeans method)</a>
</li>
        <li><a href="prml.dimreduction.html#prml.dimreduction.Autoencoder.fit">(prml.dimreduction.Autoencoder method)</a>
</li>
        <li><a href="prml.dimreduction.html#prml.dimreduction.BayesianPCA.fit">(prml.dimreduction.BayesianPCA method)</a>
</li>
        <li><a href="prml.dimreduction.html#prml.dimreduction.PCA.fit">(prml.dimreduction.PCA method)</a>
</li>
        <li><a href="prml.dimreduction.html#prml.dimreduction.autoencoder.Autoencoder.fit">(prml.dimreduction.autoencoder.Autoencoder method)</a>
</li>
        <li><a href="prml.dimreduction.html#prml.dimreduction.bayesian_pca.BayesianPCA.fit">(prml.dimreduction.bayesian_pca.BayesianPCA method)</a>
</li>
        <li><a href="prml.dimreduction.html#prml.dimreduction.pca.PCA.fit">(prml.dimreduction.pca.PCA method)</a>
</li>
        <li><a href="prml.kernel.html#prml.kernel.GaussianProcessClassifier.fit">(prml.kernel.GaussianProcessClassifier method)</a>
</li>
        <li><a href="prml.kernel.html#prml.kernel.GaussianProcessRegressor.fit">(prml.kernel.GaussianProcessRegressor method)</a>
</li>
        <li><a href="prml.kernel.html#prml.kernel.RelevanceVectorClassifier.fit">(prml.kernel.RelevanceVectorClassifier method)</a>
</li>
        <li><a href="prml.kernel.html#prml.kernel.RelevanceVectorRegressor.fit">(prml.kernel.RelevanceVectorRegressor method)</a>
</li>
        <li><a href="prml.kernel.html#prml.kernel.SupportVectorClassifier.fit">(prml.kernel.SupportVectorClassifier method)</a>
</li>
        <li><a href="prml.kernel.html#prml.kernel.gaussian_process_classifier.GaussianProcessClassifier.fit">(prml.kernel.gaussian_process_classifier.GaussianProcessClassifier method)</a>
</li>
        <li><a href="prml.kernel.html#prml.kernel.gaussian_process_regressor.GaussianProcessRegressor.fit">(prml.kernel.gaussian_process_regressor.GaussianProcessRegressor method)</a>
</li>
        <li><a href="prml.kernel.html#prml.kernel.relevance_vector_classifier.RelevanceVectorClassifier.fit">(prml.kernel.relevance_vector_classifier.RelevanceVectorClassifier method)</a>
</li>
        <li><a href="prml.kernel.html#prml.kernel.relevance_vector_regressor.RelevanceVectorRegressor.fit">(prml.kernel.relevance_vector_regressor.RelevanceVectorRegressor method)</a>
</li>
        <li><a href="prml.kernel.html#prml.kernel.support_vector_classifier.SupportVectorClassifier.fit">(prml.kernel.support_vector_classifier.SupportVectorClassifier method)</a>
</li>
        <li><a href="prml.linear.html#prml.linear.BayesianLogisticRegression.fit">(prml.linear.BayesianLogisticRegression method)</a>
</li>
        <li><a href="prml.linear.html#prml.linear.BayesianRegression.fit">(prml.linear.BayesianRegression method)</a>
</li>
        <li><a href="prml.linear.html#prml.linear.EmpiricalBayesRegression.fit">(prml.linear.EmpiricalBayesRegression method)</a>
</li>
        <li><a href="prml.linear.html#prml.linear.FishersLinearDiscriminant.fit">(prml.linear.FishersLinearDiscriminant method)</a>
</li>
        <li><a href="prml.linear.html#prml.linear.LeastSquaresClassifier.fit">(prml.linear.LeastSquaresClassifier method)</a>
</li>
        <li><a href="prml.linear.html#prml.linear.LinearRegression.fit">(prml.linear.LinearRegression method)</a>
</li>
        <li><a href="prml.linear.html#prml.linear.LogisticRegression.fit">(prml.linear.LogisticRegression method)</a>
</li>
        <li><a href="prml.linear.html#prml.linear.Perceptron.fit">(prml.linear.Perceptron method)</a>
</li>
        <li><a href="prml.linear.html#prml.linear.RidgeRegression.fit">(prml.linear.RidgeRegression method)</a>
</li>
        <li><a href="prml.linear.html#prml.linear.SoftmaxRegression.fit">(prml.linear.SoftmaxRegression method)</a>
</li>
        <li><a href="prml.linear.html#prml.linear.VariationalLinearRegression.fit">(prml.linear.VariationalLinearRegression method)</a>
</li>
        <li><a href="prml.linear.html#prml.linear.VariationalLogisticRegression.fit">(prml.linear.VariationalLogisticRegression method)</a>
</li>
        <li><a href="prml.linear.html#prml.linear.bayesian_logistic_regression.BayesianLogisticRegression.fit">(prml.linear.bayesian_logistic_regression.BayesianLogisticRegression method)</a>
</li>
        <li><a href="prml.linear.html#prml.linear.bayesian_regression.BayesianRegression.fit">(prml.linear.bayesian_regression.BayesianRegression method)</a>
</li>
        <li><a href="prml.linear.html#prml.linear.emprical_bayes_regression.EmpiricalBayesRegression.fit">(prml.linear.emprical_bayes_regression.EmpiricalBayesRegression method)</a>
</li>
        <li><a href="prml.linear.html#prml.linear.fishers_linear_discriminant.FishersLinearDiscriminant.fit">(prml.linear.fishers_linear_discriminant.FishersLinearDiscriminant method)</a>
</li>
        <li><a href="prml.linear.html#prml.linear.least_squares_classifier.LeastSquaresClassifier.fit">(prml.linear.least_squares_classifier.LeastSquaresClassifier method)</a>
</li>
        <li><a href="prml.linear.html#prml.linear.linear_regression.LinearRegression.fit">(prml.linear.linear_regression.LinearRegression method)</a>
</li>
        <li><a href="prml.linear.html#prml.linear.logistic_regression.LogisticRegression.fit">(prml.linear.logistic_regression.LogisticRegression method)</a>
</li>
        <li><a href="prml.linear.html#prml.linear.perceptron.Perceptron.fit">(prml.linear.perceptron.Perceptron method)</a>
</li>
        <li><a href="prml.linear.html#prml.linear.ridge_regression.RidgeRegression.fit">(prml.linear.ridge_regression.RidgeRegression method)</a>
</li>
        <li><a href="prml.linear.html#prml.linear.softmax_regression.SoftmaxRegression.fit">(prml.linear.softmax_regression.SoftmaxRegression method)</a>
</li>
        <li><a href="prml.linear.html#prml.linear.variational_linear_regression.VariationalLinearRegression.fit">(prml.linear.variational_linear_regression.VariationalLinearRegression method)</a>
</li>
        <li><a href="prml.linear.html#prml.linear.variational_logistic_regression.VariationalLogisticRegression.fit">(prml.linear.variational_logistic_regression.VariationalLogisticRegression method)</a>
</li>
        <li><a href="prml.markov.html#prml.markov.Kalman.fit">(prml.markov.Kalman method)</a>
</li>
        <li><a href="prml.markov.html#prml.markov.hmm.HiddenMarkovModel.fit">(prml.markov.hmm.HiddenMarkovModel method)</a>
</li>
        <li><a href="prml.markov.html#prml.markov.kalman.Kalman.fit">(prml.markov.kalman.Kalman method)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.rv.RandomVariable.fit">(prml.rv.rv.RandomVariable method)</a>
</li>
      </ul></li>
  </ul></td>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="prml.dimreduction.html#prml.dimreduction.PCA.fit_transform">fit_transform() (prml.dimreduction.PCA method)</a>

      <ul>
        <li><a href="prml.dimreduction.html#prml.dimreduction.pca.PCA.fit_transform">(prml.dimreduction.pca.PCA method)</a>
</li>
      </ul></li>
      <li><a href="prml.nn.array.html#prml.nn.array.array.Array.flatten">flatten() (prml.nn.array.array.Array method)</a>
</li>
      <li><a href="prml.dimreduction.html#prml.dimreduction.Autoencoder.forward">forward() (prml.dimreduction.Autoencoder method)</a>

      <ul>
        <li><a href="prml.dimreduction.html#prml.dimreduction.autoencoder.Autoencoder.forward">(prml.dimreduction.autoencoder.Autoencoder method)</a>
</li>
        <li><a href="prml.nn.distribution.html#prml.nn.distribution.bernoulli.Bernoulli.forward">(prml.nn.distribution.bernoulli.Bernoulli method)</a>
</li>
        <li><a href="prml.nn.distribution.html#prml.nn.distribution.gaussian.Gaussian.forward">(prml.nn.distribution.gaussian.Gaussian method)</a>
</li>
        <li><a href="prml.nn.distribution.html#prml.nn.distribution.gaussian.GaussianRadial.forward">(prml.nn.distribution.gaussian.GaussianRadial method)</a>
</li>
        <li><a href="prml.nn.html#prml.nn.function.Function.forward">(prml.nn.function.Function method)</a>
</li>
      </ul></li>
      <li><a href="prml.markov.html#prml.markov.hmm.HiddenMarkovModel.forward_backward">forward_backward() (prml.markov.hmm.HiddenMarkovModel method)</a>
</li>
      <li><a href="prml.nn.html#prml.nn.function.Function">Function (class in prml.nn.function)</a>
</li>
  </ul></td>
</tr></table>

<h2 id="G">G</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="prml.rv.html#prml.rv.Gamma">Gamma (class in prml.rv)</a>

      <ul>
        <li><a href="prml.rv.html#prml.rv.gamma.Gamma">(class in prml.rv.gamma)</a>
</li>
      </ul></li>
      <li><a href="prml.nn.distribution.html#prml.nn.distribution.gaussian.Gaussian">Gaussian (class in prml.nn.distribution.gaussian)</a>

      <ul>
        <li><a href="prml.rv.html#prml.rv.Gaussian">(class in prml.rv)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.gaussian.Gaussian">(class in prml.rv.gaussian)</a>
</li>
      </ul></li>
      <li><a href="prml.preprocess.html#prml.preprocess.GaussianFeature">GaussianFeature (class in prml.preprocess)</a>

      <ul>
        <li><a href="prml.preprocess.html#prml.preprocess.gaussian.GaussianFeature">(class in prml.preprocess.gaussian)</a>
</li>
      </ul></li>
      <li><a href="prml.markov.html#prml.markov.GaussianHMM">GaussianHMM (class in prml.markov)</a>

      <ul>
        <li><a href="prml.markov.html#prml.markov.gaussian_hmm.GaussianHMM">(class in prml.markov.gaussian_hmm)</a>
</li>
      </ul></li>
  </ul></td>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="prml.nn.distribution.html#prml.nn.distribution.gaussian.GaussianLogPDF">GaussianLogPDF (class in prml.nn.distribution.gaussian)</a>
</li>
      <li><a href="prml.kernel.html#prml.kernel.GaussianProcessClassifier">GaussianProcessClassifier (class in prml.kernel)</a>

      <ul>
        <li><a href="prml.kernel.html#prml.kernel.gaussian_process_classifier.GaussianProcessClassifier">(class in prml.kernel.gaussian_process_classifier)</a>
</li>
      </ul></li>
      <li><a href="prml.kernel.html#prml.kernel.GaussianProcessRegressor">GaussianProcessRegressor (class in prml.kernel)</a>

      <ul>
        <li><a href="prml.kernel.html#prml.kernel.gaussian_process_regressor.GaussianProcessRegressor">(class in prml.kernel.gaussian_process_regressor)</a>
</li>
      </ul></li>
      <li><a href="prml.nn.distribution.html#prml.nn.distribution.gaussian.GaussianRadial">GaussianRadial (class in prml.nn.distribution.gaussian)</a>
</li>
      <li><a href="prml.rv.html#prml.rv.variational_gaussian_mixture.VariationalGaussianMixture.get_params">get_params() (prml.rv.variational_gaussian_mixture.VariationalGaussianMixture method)</a>

      <ul>
        <li><a href="prml.rv.html#prml.rv.VariationalGaussianMixture.get_params">(prml.rv.VariationalGaussianMixture method)</a>
</li>
      </ul></li>
      <li><a href="prml.nn.optimizer.html#prml.nn.optimizer.gradient.Gradient">Gradient (class in prml.nn.optimizer.gradient)</a>
</li>
  </ul></td>
</tr></table>

<h2 id="H">H</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="prml.markov.html#prml.markov.hmm.HiddenMarkovModel">HiddenMarkovModel (class in prml.markov.hmm)</a>
</li>
  </ul></td>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="prml.rv.html#prml.rv.Uniform.high">high (prml.rv.Uniform attribute)</a>

      <ul>
        <li><a href="prml.rv.html#prml.rv.uniform.Uniform.high">(prml.rv.uniform.Uniform attribute)</a>
</li>
      </ul></li>
  </ul></td>
</tr></table>

<h2 id="I">I</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="prml.nn.image.html#prml.nn.image.util.img2patch">img2patch() (in module prml.nn.image.util)</a>
</li>
      <li><a href="prml.nn.optimizer.html#prml.nn.optimizer.optimizer.Optimizer.increment_iter_count">increment_iter_count() (prml.nn.optimizer.optimizer.Optimizer method)</a>
</li>
      <li><a href="prml.nn.distribution.html#prml.nn.distribution.bernoulli.Bernoulli.is_categorical">is_categorical (prml.nn.distribution.bernoulli.Bernoulli attribute)</a>

      <ul>
        <li><a href="prml.nn.distribution.html#prml.nn.distribution.categorical.Categorical.is_categorical">(prml.nn.distribution.categorical.Categorical attribute)</a>
</li>
        <li><a href="prml.nn.distribution.html#prml.nn.distribution.distribution.Distribution.is_categorical">(prml.nn.distribution.distribution.Distribution attribute)</a>
</li>
      </ul></li>
  </ul></td>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="prml.nn.html#prml.nn.config.Config.is_updating_bn">is_updating_bn (prml.nn.config.Config attribute)</a>
</li>
  </ul></td>
</tr></table>

<h2 id="J">J</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="prml.rv.html#prml.rv.multivariate_gaussian_mixture.MultivariateGaussianMixture.joint_proba">joint_proba() (prml.rv.multivariate_gaussian_mixture.MultivariateGaussianMixture method)</a>

      <ul>
        <li><a href="prml.rv.html#prml.rv.MultivariateGaussianMixture.joint_proba">(prml.rv.MultivariateGaussianMixture method)</a>
</li>
      </ul></li>
  </ul></td>
</tr></table>

<h2 id="K">K</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="prml.markov.html#prml.markov.Kalman">Kalman (class in prml.markov)</a>

      <ul>
        <li><a href="prml.markov.html#prml.markov.kalman.Kalman">(class in prml.markov.kalman)</a>
</li>
      </ul></li>
      <li><a href="prml.markov.html#prml.markov.kalman_filter">kalman_filter() (in module prml.markov)</a>

      <ul>
        <li><a href="prml.markov.html#prml.markov.kalman.kalman_filter">(in module prml.markov.kalman)</a>
</li>
      </ul></li>
      <li><a href="prml.markov.html#prml.markov.kalman_smoother">kalman_smoother() (in module prml.markov)</a>

      <ul>
        <li><a href="prml.markov.html#prml.markov.kalman.kalman_smoother">(in module prml.markov.kalman)</a>
</li>
      </ul></li>
      <li><a href="prml.kernel.html#prml.kernel.kernel.Kernel">Kernel (class in prml.kernel.kernel)</a>
</li>
      <li><a href="prml.nn.image.html#prml.nn.image.convolve2d.Convolve2d.kernel">kernel (prml.nn.image.convolve2d.Convolve2d attribute)</a>

      <ul>
        <li><a href="prml.nn.image.html#prml.nn.image.deconvolve2d.Deconvolve2d.kernel">(prml.nn.image.deconvolve2d.Deconvolve2d attribute)</a>
</li>
      </ul></li>
  </ul></td>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="prml.nn.loss.html#prml.nn.loss.kl.kl_bernoulli">kl_bernoulli() (in module prml.nn.loss.kl)</a>
</li>
      <li><a href="prml.nn.loss.html#prml.nn.loss.kl.kl_categorical">kl_categorical() (in module prml.nn.loss.kl)</a>
</li>
      <li><a href="prml.nn.loss.html#prml.nn.loss.kl.kl_divergence">kl_divergence() (in module prml.nn.loss.kl)</a>
</li>
      <li><a href="prml.nn.loss.html#prml.nn.loss.kl.kl_gaussian">kl_gaussian() (in module prml.nn.loss.kl)</a>
</li>
      <li><a href="prml.nn.random.html#prml.nn.random.random.RandomVariable.KLqp">KLqp() (prml.nn.random.random.RandomVariable method)</a>
</li>
      <li><a href="prml.clustering.html#prml.clustering.KMeans">KMeans (class in prml.clustering)</a>

      <ul>
        <li><a href="prml.clustering.html#prml.clustering.k_means.KMeans">(class in prml.clustering.k_means)</a>
</li>
      </ul></li>
  </ul></td>
</tr></table>

<h2 id="L">L</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="prml.preprocess.html#prml.preprocess.LabelTransformer">LabelTransformer (class in prml.preprocess)</a>

      <ul>
        <li><a href="prml.preprocess.html#prml.preprocess.label_transformer.LabelTransformer">(class in prml.preprocess.label_transformer)</a>
</li>
      </ul></li>
      <li><a href="prml.kernel.html#prml.kernel.support_vector_classifier.SupportVectorClassifier.lagrangian_function">lagrangian_function() (prml.kernel.support_vector_classifier.SupportVectorClassifier method)</a>

      <ul>
        <li><a href="prml.kernel.html#prml.kernel.SupportVectorClassifier.lagrangian_function">(prml.kernel.SupportVectorClassifier method)</a>
</li>
      </ul></li>
      <li><a href="prml.linear.html#prml.linear.LeastSquaresClassifier">LeastSquaresClassifier (class in prml.linear)</a>

      <ul>
        <li><a href="prml.linear.html#prml.linear.least_squares_classifier.LeastSquaresClassifier">(class in prml.linear.least_squares_classifier)</a>
</li>
      </ul></li>
      <li><a href="prml.markov.html#prml.markov.categorical_hmm.CategoricalHMM.likelihood">likelihood() (prml.markov.categorical_hmm.CategoricalHMM method)</a>

      <ul>
        <li><a href="prml.markov.html#prml.markov.CategoricalHMM.likelihood">(prml.markov.CategoricalHMM method)</a>
</li>
        <li><a href="prml.markov.html#prml.markov.GaussianHMM.likelihood">(prml.markov.GaussianHMM method)</a>
</li>
        <li><a href="prml.markov.html#prml.markov.gaussian_hmm.GaussianHMM.likelihood">(prml.markov.gaussian_hmm.GaussianHMM method)</a>
</li>
      </ul></li>
      <li><a href="prml.linear.html#prml.linear.LinearRegression">LinearRegression (class in prml.linear)</a>

      <ul>
        <li><a href="prml.linear.html#prml.linear.linear_regression.LinearRegression">(class in prml.linear.linear_regression)</a>
</li>
      </ul></li>
      <li><a href="prml.nn.io.html#prml.nn.io.io.load_object">load_object() (in module prml.nn.io.io)</a>
</li>
      <li><a href="prml.nn.io.html#prml.nn.io.io.load_parameter">load_parameter() (in module prml.nn.io.io)</a>
</li>
      <li><a href="prml.nn.math.html#prml.nn.math.log.Log">Log (class in prml.nn.math.log)</a>
</li>
  </ul></td>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="prml.nn.math.html#prml.nn.math.log.log">log() (in module prml.nn.math.log)</a>
</li>
      <li><a href="prml.nn.distribution.html#prml.nn.distribution.gaussian.GaussianLogPDF.log2pi">log2pi (prml.nn.distribution.gaussian.GaussianLogPDF attribute)</a>
</li>
      <li><a href="prml.linear.html#prml.linear.EmpiricalBayesRegression.log_evidence">log_evidence() (prml.linear.EmpiricalBayesRegression method)</a>

      <ul>
        <li><a href="prml.linear.html#prml.linear.emprical_bayes_regression.EmpiricalBayesRegression.log_evidence">(prml.linear.emprical_bayes_regression.EmpiricalBayesRegression method)</a>
</li>
      </ul></li>
      <li><a href="prml.kernel.html#prml.kernel.gaussian_process_regressor.GaussianProcessRegressor.log_likelihood">log_likelihood() (prml.kernel.gaussian_process_regressor.GaussianProcessRegressor method)</a>

      <ul>
        <li><a href="prml.kernel.html#prml.kernel.GaussianProcessRegressor.log_likelihood">(prml.kernel.GaussianProcessRegressor method)</a>
</li>
      </ul></li>
      <li><a href="prml.nn.distribution.html#prml.nn.distribution.distribution.Distribution.log_pdf">log_pdf() (prml.nn.distribution.distribution.Distribution method)</a>

      <ul>
        <li><a href="prml.nn.random.html#prml.nn.random.random.RandomVariable.log_pdf">(prml.nn.random.random.RandomVariable method)</a>
</li>
      </ul></li>
      <li><a href="prml.nn.nonlinear.html#prml.nn.nonlinear.log_softmax.log_softmax">log_softmax() (in module prml.nn.nonlinear.log_softmax)</a>
</li>
      <li><a href="prml.linear.html#prml.linear.LogisticRegression">LogisticRegression (class in prml.linear)</a>

      <ul>
        <li><a href="prml.linear.html#prml.linear.logistic_regression.LogisticRegression">(class in prml.linear.logistic_regression)</a>
</li>
      </ul></li>
      <li><a href="prml.nn.nonlinear.html#prml.nn.nonlinear.logit.Logit">Logit (class in prml.nn.nonlinear.logit)</a>
</li>
      <li><a href="prml.nn.nonlinear.html#prml.nn.nonlinear.logit.logit">logit() (in module prml.nn.nonlinear.logit)</a>
</li>
      <li><a href="prml.nn.nonlinear.html#prml.nn.nonlinear.log_softmax.LogSoftmax">LogSoftmax (class in prml.nn.nonlinear.log_softmax)</a>
</li>
      <li><a href="prml.rv.html#prml.rv.Uniform.low">low (prml.rv.Uniform attribute)</a>

      <ul>
        <li><a href="prml.rv.html#prml.rv.uniform.Uniform.low">(prml.rv.uniform.Uniform attribute)</a>
</li>
      </ul></li>
  </ul></td>
</tr></table>

<h2 id="M">M</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="prml.nn.math.html#prml.nn.math.matmul.Matmul">Matmul (class in prml.nn.math.matmul)</a>
</li>
      <li><a href="prml.nn.math.html#prml.nn.math.matmul.matmul">matmul() (in module prml.nn.math.matmul)</a>
</li>
      <li><a href="prml.nn.image.html#prml.nn.image.max_pooling2d.max_pooling2d">max_pooling2d() (in module prml.nn.image.max_pooling2d)</a>
</li>
      <li><a href="prml.dimreduction.html#prml.dimreduction.bayesian_pca.BayesianPCA.maximize">maximize() (prml.dimreduction.bayesian_pca.BayesianPCA method)</a>

      <ul>
        <li><a href="prml.dimreduction.html#prml.dimreduction.BayesianPCA.maximize">(prml.dimreduction.BayesianPCA method)</a>
</li>
        <li><a href="prml.markov.html#prml.markov.CategoricalHMM.maximize">(prml.markov.CategoricalHMM method)</a>
</li>
        <li><a href="prml.markov.html#prml.markov.GaussianHMM.maximize">(prml.markov.GaussianHMM method)</a>
</li>
        <li><a href="prml.markov.html#prml.markov.categorical_hmm.CategoricalHMM.maximize">(prml.markov.categorical_hmm.CategoricalHMM method)</a>
</li>
        <li><a href="prml.markov.html#prml.markov.gaussian_hmm.GaussianHMM.maximize">(prml.markov.gaussian_hmm.GaussianHMM method)</a>
</li>
        <li><a href="prml.nn.optimizer.html#prml.nn.optimizer.optimizer.Optimizer.maximize">(prml.nn.optimizer.optimizer.Optimizer method)</a>
</li>
      </ul></li>
      <li><a href="prml.nn.image.html#prml.nn.image.max_pooling2d.MaxPooling2d">MaxPooling2d (class in prml.nn.image.max_pooling2d)</a>
</li>
      <li><a href="prml.dimreduction.html#prml.dimreduction.PCA.mean">mean (prml.dimreduction.PCA attribute)</a>

      <ul>
        <li><a href="prml.dimreduction.html#prml.dimreduction.pca.PCA.mean">(prml.dimreduction.pca.PCA attribute)</a>
</li>
        <li><a href="prml.kernel.html#prml.kernel.RelevanceVectorClassifier.mean">(prml.kernel.RelevanceVectorClassifier attribute)</a>
</li>
        <li><a href="prml.kernel.html#prml.kernel.RelevanceVectorRegressor.mean">(prml.kernel.RelevanceVectorRegressor attribute)</a>
</li>
        <li><a href="prml.kernel.html#prml.kernel.relevance_vector_classifier.RelevanceVectorClassifier.mean">(prml.kernel.relevance_vector_classifier.RelevanceVectorClassifier attribute)</a>
</li>
        <li><a href="prml.kernel.html#prml.kernel.relevance_vector_regressor.RelevanceVectorRegressor.mean">(prml.kernel.relevance_vector_regressor.RelevanceVectorRegressor attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.Uniform.mean">(prml.rv.Uniform attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.uniform.Uniform.mean">(prml.rv.uniform.Uniform attribute)</a>
</li>
      </ul></li>
      <li><a href="prml.nn.math.html#prml.nn.math.mean.mean">mean() (in module prml.nn.math.mean)</a>

      <ul>
        <li><a href="prml.nn.array.html#prml.nn.array.array.Array.mean">(prml.nn.array.array.Array method)</a>
</li>
      </ul></li>
      <li><a href="prml.sampling.html#prml.sampling.metropolis">metropolis() (in module prml.sampling)</a>

      <ul>
        <li><a href="prml.sampling.html#prml.sampling.metropolis.metropolis">(in module prml.sampling.metropolis)</a>
</li>
      </ul></li>
      <li><a href="prml.sampling.html#prml.sampling.metropolis_hastings">metropolis_hastings() (in module prml.sampling)</a>

      <ul>
        <li><a href="prml.sampling.html#prml.sampling.metropolis_hastings.metropolis_hastings">(in module prml.sampling.metropolis_hastings)</a>
</li>
      </ul></li>
  </ul></td>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="prml.nn.optimizer.html#prml.nn.optimizer.optimizer.Optimizer.minimize">minimize() (prml.nn.optimizer.optimizer.Optimizer method)</a>
</li>
      <li><a href="prml.nn.optimizer.html#prml.nn.optimizer.momentum.Momentum">Momentum (class in prml.nn.optimizer.momentum)</a>
</li>
      <li><a href="prml.rv.html#prml.rv.Bernoulli.mu">mu (prml.rv.Bernoulli attribute)</a>

      <ul>
        <li><a href="prml.rv.html#prml.rv.BernoulliMixture.mu">(prml.rv.BernoulliMixture attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.Categorical.mu">(prml.rv.Categorical attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.Gaussian.mu">(prml.rv.Gaussian attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.MultivariateGaussian.mu">(prml.rv.MultivariateGaussian attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.MultivariateGaussianMixture.mu">(prml.rv.MultivariateGaussianMixture attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.StudentsT.mu">(prml.rv.StudentsT attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.VariationalGaussianMixture.mu">(prml.rv.VariationalGaussianMixture attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.bernoulli.Bernoulli.mu">(prml.rv.bernoulli.Bernoulli attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.bernoulli_mixture.BernoulliMixture.mu">(prml.rv.bernoulli_mixture.BernoulliMixture attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.categorical.Categorical.mu">(prml.rv.categorical.Categorical attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.gaussian.Gaussian.mu">(prml.rv.gaussian.Gaussian attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.multivariate_gaussian.MultivariateGaussian.mu">(prml.rv.multivariate_gaussian.MultivariateGaussian attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.multivariate_gaussian_mixture.MultivariateGaussianMixture.mu">(prml.rv.multivariate_gaussian_mixture.MultivariateGaussianMixture attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.students_t.StudentsT.mu">(prml.rv.students_t.StudentsT attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.variational_gaussian_mixture.VariationalGaussianMixture.mu">(prml.rv.variational_gaussian_mixture.VariationalGaussianMixture attribute)</a>
</li>
      </ul></li>
      <li><a href="prml.nn.math.html#prml.nn.math.multiply.Multiply">Multiply (class in prml.nn.math.multiply)</a>
</li>
      <li><a href="prml.nn.math.html#prml.nn.math.multiply.multiply">multiply() (in module prml.nn.math.multiply)</a>
</li>
      <li><a href="prml.rv.html#prml.rv.MultivariateGaussian">MultivariateGaussian (class in prml.rv)</a>

      <ul>
        <li><a href="prml.rv.html#prml.rv.multivariate_gaussian.MultivariateGaussian">(class in prml.rv.multivariate_gaussian)</a>
</li>
      </ul></li>
      <li><a href="prml.rv.html#prml.rv.MultivariateGaussianMixture">MultivariateGaussianMixture (class in prml.rv)</a>

      <ul>
        <li><a href="prml.rv.html#prml.rv.multivariate_gaussian_mixture.MultivariateGaussianMixture">(class in prml.rv.multivariate_gaussian_mixture)</a>
</li>
      </ul></li>
  </ul></td>
</tr></table>

<h2 id="N">N</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="prml.preprocess.html#prml.preprocess.label_transformer.LabelTransformer.n_classes">n_classes (prml.preprocess.label_transformer.LabelTransformer attribute)</a>, <a href="prml.preprocess.html#prml.preprocess.label_transformer.LabelTransformer.n_classes">[1]</a>

      <ul>
        <li><a href="prml.preprocess.html#prml.preprocess.LabelTransformer.n_classes">(prml.preprocess.LabelTransformer attribute)</a>, <a href="prml.preprocess.html#prml.preprocess.LabelTransformer.n_classes">[1]</a>
</li>
      </ul></li>
      <li><a href="prml.linear.html#prml.linear.variational_linear_regression.VariationalLinearRegression.n_iter">n_iter (prml.linear.variational_linear_regression.VariationalLinearRegression attribute)</a>

      <ul>
        <li><a href="prml.linear.html#prml.linear.VariationalLinearRegression.n_iter">(prml.linear.VariationalLinearRegression attribute)</a>
</li>
      </ul></li>
      <li><a href="prml.nn.array.html#prml.nn.array.array.Array.ndim">ndim (prml.nn.array.array.Array attribute)</a>

      <ul>
        <li><a href="prml.rv.html#prml.rv.Bernoulli.ndim">(prml.rv.Bernoulli attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.Beta.ndim">(prml.rv.Beta attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.Categorical.ndim">(prml.rv.Categorical attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.Dirichlet.ndim">(prml.rv.Dirichlet attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.Gamma.ndim">(prml.rv.Gamma attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.Gaussian.ndim">(prml.rv.Gaussian attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.MultivariateGaussian.ndim">(prml.rv.MultivariateGaussian attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.StudentsT.ndim">(prml.rv.StudentsT attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.Uniform.ndim">(prml.rv.Uniform attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.bernoulli.Bernoulli.ndim">(prml.rv.bernoulli.Bernoulli attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.beta.Beta.ndim">(prml.rv.beta.Beta attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.categorical.Categorical.ndim">(prml.rv.categorical.Categorical attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.dirichlet.Dirichlet.ndim">(prml.rv.dirichlet.Dirichlet attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.gamma.Gamma.ndim">(prml.rv.gamma.Gamma attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.gaussian.Gaussian.ndim">(prml.rv.gaussian.Gaussian attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.multivariate_gaussian.MultivariateGaussian.ndim">(prml.rv.multivariate_gaussian.MultivariateGaussian attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.students_t.StudentsT.ndim">(prml.rv.students_t.StudentsT attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.uniform.Uniform.ndim">(prml.rv.uniform.Uniform attribute)</a>
</li>
      </ul></li>
  </ul></td>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="prml.nn.math.html#prml.nn.math.negative.Negative">Negative (class in prml.nn.math.negative)</a>
</li>
      <li><a href="prml.nn.math.html#prml.nn.math.negative.negative">negative() (in module prml.nn.math.negative)</a>
</li>
      <li><a href="prml.nn.html#prml.nn.network.Network">Network (class in prml.nn.network)</a>
</li>
      <li><a href="prml.nn.random.html#prml.nn.random.normal.normal">normal() (in module prml.nn.random.normal)</a>
</li>
  </ul></td>
</tr></table>

<h2 id="O">O</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="prml.bayesnet.html#prml.bayesnet.discrete.DiscreteVariable.observe">observe() (prml.bayesnet.discrete.DiscreteVariable method)</a>

      <ul>
        <li><a href="prml.bayesnet.html#prml.bayesnet.DiscreteVariable.observe">(prml.bayesnet.DiscreteVariable method)</a>
</li>
      </ul></li>
  </ul></td>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="prml.nn.array.html#prml.nn.array.ones.ones">ones() (in module prml.nn.array.ones)</a>
</li>
      <li><a href="prml.nn.optimizer.html#prml.nn.optimizer.optimizer.Optimizer.optimize">optimize() (prml.nn.optimizer.optimizer.Optimizer method)</a>
</li>
      <li><a href="prml.nn.optimizer.html#prml.nn.optimizer.optimizer.Optimizer">Optimizer (class in prml.nn.optimizer.optimizer)</a>
</li>
  </ul></td>
</tr></table>

<h2 id="P">P</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="prml.markov.html#prml.markov.Particle">Particle (class in prml.markov)</a>

      <ul>
        <li><a href="prml.markov.html#prml.markov.particle.Particle">(class in prml.markov.particle)</a>
</li>
      </ul></li>
      <li><a href="prml.nn.image.html#prml.nn.image.util.patch2img">patch2img() (in module prml.nn.image.util)</a>
</li>
      <li><a href="prml.nn.image.html#prml.nn.image.util.patch2img_no_overlap">patch2img_no_overlap() (in module prml.nn.image.util)</a>
</li>
      <li><a href="prml.dimreduction.html#prml.dimreduction.PCA">PCA (class in prml.dimreduction)</a>

      <ul>
        <li><a href="prml.dimreduction.html#prml.dimreduction.pca.PCA">(class in prml.dimreduction.pca)</a>
</li>
      </ul></li>
      <li><a href="prml.nn.distribution.html#prml.nn.distribution.distribution.Distribution.pdf">pdf() (prml.nn.distribution.distribution.Distribution method)</a>

      <ul>
        <li><a href="prml.nn.random.html#prml.nn.random.random.RandomVariable.pdf">(prml.nn.random.random.RandomVariable method)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.rv.RandomVariable.pdf">(prml.rv.rv.RandomVariable method)</a>
</li>
      </ul></li>
      <li><a href="prml.linear.html#prml.linear.Perceptron">Perceptron (class in prml.linear)</a>

      <ul>
        <li><a href="prml.linear.html#prml.linear.perceptron.Perceptron">(class in prml.linear.perceptron)</a>
</li>
      </ul></li>
      <li><a href="prml.preprocess.html#prml.preprocess.PolynomialFeature">PolynomialFeature (class in prml.preprocess)</a>

      <ul>
        <li><a href="prml.preprocess.html#prml.preprocess.polynomial.PolynomialFeature">(class in prml.preprocess.polynomial)</a>
</li>
      </ul></li>
      <li><a href="prml.kernel.html#prml.kernel.PolynomialKernel">PolynomialKernel (class in prml.kernel)</a>

      <ul>
        <li><a href="prml.kernel.html#prml.kernel.polynomial.PolynomialKernel">(class in prml.kernel.polynomial)</a>
</li>
      </ul></li>
      <li><a href="prml.nn.math.html#prml.nn.math.power.Power">Power (class in prml.nn.math.power)</a>
</li>
      <li><a href="prml.nn.math.html#prml.nn.math.power.power">power() (in module prml.nn.math.power)</a>
</li>
      <li><a href="prml.kernel.html#prml.kernel.gaussian_process_regressor.GaussianProcessRegressor.precision">precision (prml.kernel.gaussian_process_regressor.GaussianProcessRegressor attribute)</a>

      <ul>
        <li><a href="prml.kernel.html#prml.kernel.GaussianProcessRegressor.precision">(prml.kernel.GaussianProcessRegressor attribute)</a>
</li>
      </ul></li>
      <li><a href="prml.clustering.html#prml.clustering.k_means.KMeans.predict">predict() (prml.clustering.k_means.KMeans method)</a>

      <ul>
        <li><a href="prml.clustering.html#prml.clustering.KMeans.predict">(prml.clustering.KMeans method)</a>
</li>
        <li><a href="prml.kernel.html#prml.kernel.GaussianProcessClassifier.predict">(prml.kernel.GaussianProcessClassifier method)</a>
</li>
        <li><a href="prml.kernel.html#prml.kernel.GaussianProcessRegressor.predict">(prml.kernel.GaussianProcessRegressor method)</a>
</li>
        <li><a href="prml.kernel.html#prml.kernel.RelevanceVectorClassifier.predict">(prml.kernel.RelevanceVectorClassifier method)</a>
</li>
        <li><a href="prml.kernel.html#prml.kernel.RelevanceVectorRegressor.predict">(prml.kernel.RelevanceVectorRegressor method)</a>
</li>
        <li><a href="prml.kernel.html#prml.kernel.SupportVectorClassifier.predict">(prml.kernel.SupportVectorClassifier method)</a>
</li>
        <li><a href="prml.kernel.html#prml.kernel.gaussian_process_classifier.GaussianProcessClassifier.predict">(prml.kernel.gaussian_process_classifier.GaussianProcessClassifier method)</a>
</li>
        <li><a href="prml.kernel.html#prml.kernel.gaussian_process_regressor.GaussianProcessRegressor.predict">(prml.kernel.gaussian_process_regressor.GaussianProcessRegressor method)</a>
</li>
        <li><a href="prml.kernel.html#prml.kernel.relevance_vector_classifier.RelevanceVectorClassifier.predict">(prml.kernel.relevance_vector_classifier.RelevanceVectorClassifier method)</a>
</li>
        <li><a href="prml.kernel.html#prml.kernel.relevance_vector_regressor.RelevanceVectorRegressor.predict">(prml.kernel.relevance_vector_regressor.RelevanceVectorRegressor method)</a>
</li>
        <li><a href="prml.kernel.html#prml.kernel.support_vector_classifier.SupportVectorClassifier.predict">(prml.kernel.support_vector_classifier.SupportVectorClassifier method)</a>
</li>
        <li><a href="prml.linear.html#prml.linear.BayesianRegression.predict">(prml.linear.BayesianRegression method)</a>
</li>
        <li><a href="prml.linear.html#prml.linear.LinearRegression.predict">(prml.linear.LinearRegression method)</a>
</li>
        <li><a href="prml.linear.html#prml.linear.RidgeRegression.predict">(prml.linear.RidgeRegression method)</a>
</li>
        <li><a href="prml.linear.html#prml.linear.VariationalLinearRegression.predict">(prml.linear.VariationalLinearRegression method)</a>
</li>
        <li><a href="prml.linear.html#prml.linear.bayesian_regression.BayesianRegression.predict">(prml.linear.bayesian_regression.BayesianRegression method)</a>
</li>
        <li><a href="prml.linear.html#prml.linear.linear_regression.LinearRegression.predict">(prml.linear.linear_regression.LinearRegression method)</a>
</li>
        <li><a href="prml.linear.html#prml.linear.ridge_regression.RidgeRegression.predict">(prml.linear.ridge_regression.RidgeRegression method)</a>
</li>
        <li><a href="prml.linear.html#prml.linear.variational_linear_regression.VariationalLinearRegression.predict">(prml.linear.variational_linear_regression.VariationalLinearRegression method)</a>
</li>
        <li><a href="prml.markov.html#prml.markov.Kalman.predict">(prml.markov.Kalman method)</a>
</li>
        <li><a href="prml.markov.html#prml.markov.Particle.predict">(prml.markov.Particle method)</a>
</li>
        <li><a href="prml.markov.html#prml.markov.kalman.Kalman.predict">(prml.markov.kalman.Kalman method)</a>
</li>
        <li><a href="prml.markov.html#prml.markov.particle.Particle.predict">(prml.markov.particle.Particle method)</a>
</li>
      </ul></li>
      <li><a href="prml.kernel.html#prml.kernel.relevance_vector_classifier.RelevanceVectorClassifier.predict_proba">predict_proba() (prml.kernel.relevance_vector_classifier.RelevanceVectorClassifier method)</a>

      <ul>
        <li><a href="prml.kernel.html#prml.kernel.RelevanceVectorClassifier.predict_proba">(prml.kernel.RelevanceVectorClassifier method)</a>
</li>
      </ul></li>
      <li><a href="prml.html#module-prml">prml (module)</a>
</li>
      <li><a href="prml.bayesnet.html#module-prml.bayesnet">prml.bayesnet (module)</a>
</li>
      <li><a href="prml.bayesnet.html#module-prml.bayesnet.discrete">prml.bayesnet.discrete (module)</a>
</li>
      <li><a href="prml.bayesnet.html#module-prml.bayesnet.probability_function">prml.bayesnet.probability_function (module)</a>
</li>
      <li><a href="prml.bayesnet.html#module-prml.bayesnet.random_variable">prml.bayesnet.random_variable (module)</a>
</li>
      <li><a href="prml.clustering.html#module-prml.clustering">prml.clustering (module)</a>
</li>
      <li><a href="prml.clustering.html#module-prml.clustering.k_means">prml.clustering.k_means (module)</a>
</li>
      <li><a href="prml.dimreduction.html#module-prml.dimreduction">prml.dimreduction (module)</a>
</li>
      <li><a href="prml.dimreduction.html#module-prml.dimreduction.autoencoder">prml.dimreduction.autoencoder (module)</a>
</li>
      <li><a href="prml.dimreduction.html#module-prml.dimreduction.bayesian_pca">prml.dimreduction.bayesian_pca (module)</a>
</li>
      <li><a href="prml.dimreduction.html#module-prml.dimreduction.pca">prml.dimreduction.pca (module)</a>
</li>
      <li><a href="prml.kernel.html#module-prml.kernel">prml.kernel (module)</a>
</li>
      <li><a href="prml.kernel.html#module-prml.kernel.gaussian_process_classifier">prml.kernel.gaussian_process_classifier (module)</a>
</li>
      <li><a href="prml.kernel.html#module-prml.kernel.gaussian_process_regressor">prml.kernel.gaussian_process_regressor (module)</a>
</li>
      <li><a href="prml.kernel.html#module-prml.kernel.kernel">prml.kernel.kernel (module)</a>
</li>
      <li><a href="prml.kernel.html#module-prml.kernel.polynomial">prml.kernel.polynomial (module)</a>
</li>
      <li><a href="prml.kernel.html#module-prml.kernel.rbf">prml.kernel.rbf (module)</a>
</li>
      <li><a href="prml.kernel.html#module-prml.kernel.relevance_vector_classifier">prml.kernel.relevance_vector_classifier (module)</a>
</li>
      <li><a href="prml.kernel.html#module-prml.kernel.relevance_vector_regressor">prml.kernel.relevance_vector_regressor (module)</a>
</li>
      <li><a href="prml.kernel.html#module-prml.kernel.support_vector_classifier">prml.kernel.support_vector_classifier (module)</a>
</li>
      <li><a href="prml.linear.html#module-prml.linear">prml.linear (module)</a>
</li>
      <li><a href="prml.linear.html#module-prml.linear.bayesian_logistic_regression">prml.linear.bayesian_logistic_regression (module)</a>
</li>
      <li><a href="prml.linear.html#module-prml.linear.bayesian_regression">prml.linear.bayesian_regression (module)</a>
</li>
      <li><a href="prml.linear.html#module-prml.linear.classifier">prml.linear.classifier (module)</a>
</li>
      <li><a href="prml.linear.html#module-prml.linear.emprical_bayes_regression">prml.linear.emprical_bayes_regression (module)</a>
</li>
      <li><a href="prml.linear.html#module-prml.linear.fishers_linear_discriminant">prml.linear.fishers_linear_discriminant (module)</a>
</li>
      <li><a href="prml.linear.html#module-prml.linear.least_squares_classifier">prml.linear.least_squares_classifier (module)</a>
</li>
      <li><a href="prml.linear.html#module-prml.linear.linear_regression">prml.linear.linear_regression (module)</a>
</li>
      <li><a href="prml.linear.html#module-prml.linear.logistic_regression">prml.linear.logistic_regression (module)</a>
</li>
      <li><a href="prml.linear.html#module-prml.linear.perceptron">prml.linear.perceptron (module)</a>
</li>
      <li><a href="prml.linear.html#module-prml.linear.regression">prml.linear.regression (module)</a>
</li>
      <li><a href="prml.linear.html#module-prml.linear.ridge_regression">prml.linear.ridge_regression (module)</a>
</li>
      <li><a href="prml.linear.html#module-prml.linear.softmax_regression">prml.linear.softmax_regression (module)</a>
</li>
      <li><a href="prml.linear.html#module-prml.linear.variational_linear_regression">prml.linear.variational_linear_regression (module)</a>
</li>
      <li><a href="prml.linear.html#module-prml.linear.variational_logistic_regression">prml.linear.variational_logistic_regression (module)</a>
</li>
      <li><a href="prml.markov.html#module-prml.markov">prml.markov (module)</a>
</li>
      <li><a href="prml.markov.html#module-prml.markov.categorical_hmm">prml.markov.categorical_hmm (module)</a>
</li>
      <li><a href="prml.markov.html#module-prml.markov.gaussian_hmm">prml.markov.gaussian_hmm (module)</a>
</li>
      <li><a href="prml.markov.html#module-prml.markov.hmm">prml.markov.hmm (module)</a>
</li>
      <li><a href="prml.markov.html#module-prml.markov.kalman">prml.markov.kalman (module)</a>
</li>
      <li><a href="prml.markov.html#module-prml.markov.particle">prml.markov.particle (module)</a>
</li>
      <li><a href="prml.markov.html#module-prml.markov.state_space_model">prml.markov.state_space_model (module)</a>
</li>
      <li><a href="prml.nn.html#module-prml.nn">prml.nn (module)</a>
</li>
      <li><a href="prml.nn.array.html#module-prml.nn.array">prml.nn.array (module)</a>
</li>
      <li><a href="prml.nn.array.html#module-prml.nn.array.array">prml.nn.array.array (module)</a>
</li>
      <li><a href="prml.nn.array.html#module-prml.nn.array.broadcast">prml.nn.array.broadcast (module)</a>
</li>
      <li><a href="prml.nn.array.html#module-prml.nn.array.ones">prml.nn.array.ones (module)</a>
</li>
      <li><a href="prml.nn.array.html#module-prml.nn.array.reshape">prml.nn.array.reshape (module)</a>
</li>
      <li><a href="prml.nn.array.html#module-prml.nn.array.zeros">prml.nn.array.zeros (module)</a>
</li>
      <li><a href="prml.nn.html#module-prml.nn.config">prml.nn.config (module)</a>
</li>
      <li><a href="prml.nn.distribution.html#module-prml.nn.distribution">prml.nn.distribution (module)</a>
</li>
  </ul></td>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="prml.nn.distribution.html#module-prml.nn.distribution.bernoulli">prml.nn.distribution.bernoulli (module)</a>
</li>
      <li><a href="prml.nn.distribution.html#module-prml.nn.distribution.categorical">prml.nn.distribution.categorical (module)</a>
</li>
      <li><a href="prml.nn.distribution.html#module-prml.nn.distribution.distribution">prml.nn.distribution.distribution (module)</a>
</li>
      <li><a href="prml.nn.distribution.html#module-prml.nn.distribution.gaussian">prml.nn.distribution.gaussian (module)</a>
</li>
      <li><a href="prml.nn.html#module-prml.nn.function">prml.nn.function (module)</a>
</li>
      <li><a href="prml.nn.image.html#module-prml.nn.image">prml.nn.image (module)</a>
</li>
      <li><a href="prml.nn.image.html#module-prml.nn.image.convolve2d">prml.nn.image.convolve2d (module)</a>
</li>
      <li><a href="prml.nn.image.html#module-prml.nn.image.deconvolve2d">prml.nn.image.deconvolve2d (module)</a>
</li>
      <li><a href="prml.nn.image.html#module-prml.nn.image.max_pooling2d">prml.nn.image.max_pooling2d (module)</a>
</li>
      <li><a href="prml.nn.image.html#module-prml.nn.image.util">prml.nn.image.util (module)</a>
</li>
      <li><a href="prml.nn.io.html#module-prml.nn.io">prml.nn.io (module)</a>
</li>
      <li><a href="prml.nn.io.html#module-prml.nn.io.io">prml.nn.io.io (module)</a>
</li>
      <li><a href="prml.nn.loss.html#module-prml.nn.loss">prml.nn.loss (module)</a>
</li>
      <li><a href="prml.nn.loss.html#module-prml.nn.loss.kl">prml.nn.loss.kl (module)</a>
</li>
      <li><a href="prml.nn.loss.html#module-prml.nn.loss.sigmoid_cross_entropy">prml.nn.loss.sigmoid_cross_entropy (module)</a>
</li>
      <li><a href="prml.nn.loss.html#module-prml.nn.loss.softmax_cross_entropy">prml.nn.loss.softmax_cross_entropy (module)</a>
</li>
      <li><a href="prml.nn.math.html#module-prml.nn.math">prml.nn.math (module)</a>
</li>
      <li><a href="prml.nn.math.html#module-prml.nn.math.add">prml.nn.math.add (module)</a>
</li>
      <li><a href="prml.nn.math.html#module-prml.nn.math.divide">prml.nn.math.divide (module)</a>
</li>
      <li><a href="prml.nn.math.html#module-prml.nn.math.exp">prml.nn.math.exp (module)</a>
</li>
      <li><a href="prml.nn.math.html#module-prml.nn.math.log">prml.nn.math.log (module)</a>
</li>
      <li><a href="prml.nn.math.html#module-prml.nn.math.matmul">prml.nn.math.matmul (module)</a>
</li>
      <li><a href="prml.nn.math.html#module-prml.nn.math.mean">prml.nn.math.mean (module)</a>
</li>
      <li><a href="prml.nn.math.html#module-prml.nn.math.multiply">prml.nn.math.multiply (module)</a>
</li>
      <li><a href="prml.nn.math.html#module-prml.nn.math.negative">prml.nn.math.negative (module)</a>
</li>
      <li><a href="prml.nn.math.html#module-prml.nn.math.power">prml.nn.math.power (module)</a>
</li>
      <li><a href="prml.nn.math.html#module-prml.nn.math.product">prml.nn.math.product (module)</a>
</li>
      <li><a href="prml.nn.math.html#module-prml.nn.math.sqrt">prml.nn.math.sqrt (module)</a>
</li>
      <li><a href="prml.nn.math.html#module-prml.nn.math.square">prml.nn.math.square (module)</a>
</li>
      <li><a href="prml.nn.math.html#module-prml.nn.math.subtract">prml.nn.math.subtract (module)</a>
</li>
      <li><a href="prml.nn.math.html#module-prml.nn.math.sum">prml.nn.math.sum (module)</a>
</li>
      <li><a href="prml.nn.html#module-prml.nn.network">prml.nn.network (module)</a>
</li>
      <li><a href="prml.nn.nonlinear.html#module-prml.nn.nonlinear">prml.nn.nonlinear (module)</a>
</li>
      <li><a href="prml.nn.nonlinear.html#module-prml.nn.nonlinear.log_softmax">prml.nn.nonlinear.log_softmax (module)</a>
</li>
      <li><a href="prml.nn.nonlinear.html#module-prml.nn.nonlinear.logit">prml.nn.nonlinear.logit (module)</a>
</li>
      <li><a href="prml.nn.nonlinear.html#module-prml.nn.nonlinear.relu">prml.nn.nonlinear.relu (module)</a>
</li>
      <li><a href="prml.nn.nonlinear.html#module-prml.nn.nonlinear.sigmoid">prml.nn.nonlinear.sigmoid (module)</a>
</li>
      <li><a href="prml.nn.nonlinear.html#module-prml.nn.nonlinear.softmax">prml.nn.nonlinear.softmax (module)</a>
</li>
      <li><a href="prml.nn.nonlinear.html#module-prml.nn.nonlinear.softplus">prml.nn.nonlinear.softplus (module)</a>
</li>
      <li><a href="prml.nn.nonlinear.html#module-prml.nn.nonlinear.tanh">prml.nn.nonlinear.tanh (module)</a>
</li>
      <li><a href="prml.nn.normalization.html#module-prml.nn.normalization">prml.nn.normalization (module)</a>
</li>
      <li><a href="prml.nn.normalization.html#module-prml.nn.normalization.batch_normalization">prml.nn.normalization.batch_normalization (module)</a>
</li>
      <li><a href="prml.nn.optimizer.html#module-prml.nn.optimizer">prml.nn.optimizer (module)</a>
</li>
      <li><a href="prml.nn.optimizer.html#module-prml.nn.optimizer.ada_delta">prml.nn.optimizer.ada_delta (module)</a>
</li>
      <li><a href="prml.nn.optimizer.html#module-prml.nn.optimizer.ada_grad">prml.nn.optimizer.ada_grad (module)</a>
</li>
      <li><a href="prml.nn.optimizer.html#module-prml.nn.optimizer.adam">prml.nn.optimizer.adam (module)</a>
</li>
      <li><a href="prml.nn.optimizer.html#module-prml.nn.optimizer.gradient">prml.nn.optimizer.gradient (module)</a>
</li>
      <li><a href="prml.nn.optimizer.html#module-prml.nn.optimizer.momentum">prml.nn.optimizer.momentum (module)</a>
</li>
      <li><a href="prml.nn.optimizer.html#module-prml.nn.optimizer.optimizer">prml.nn.optimizer.optimizer (module)</a>
</li>
      <li><a href="prml.nn.optimizer.html#module-prml.nn.optimizer.rmsprop">prml.nn.optimizer.rmsprop (module)</a>
</li>
      <li><a href="prml.nn.html#module-prml.nn.queue">prml.nn.queue (module)</a>
</li>
      <li><a href="prml.nn.random.html#module-prml.nn.random">prml.nn.random (module)</a>
</li>
      <li><a href="prml.nn.random.html#module-prml.nn.random.dropout">prml.nn.random.dropout (module)</a>
</li>
      <li><a href="prml.nn.random.html#module-prml.nn.random.normal">prml.nn.random.normal (module)</a>
</li>
      <li><a href="prml.nn.random.html#module-prml.nn.random.random">prml.nn.random.random (module)</a>
</li>
      <li><a href="prml.nn.random.html#module-prml.nn.random.uniform">prml.nn.random.uniform (module)</a>
</li>
      <li><a href="prml.preprocess.html#module-prml.preprocess">prml.preprocess (module)</a>
</li>
      <li><a href="prml.preprocess.html#module-prml.preprocess.gaussian">prml.preprocess.gaussian (module)</a>
</li>
      <li><a href="prml.preprocess.html#module-prml.preprocess.label_transformer">prml.preprocess.label_transformer (module)</a>
</li>
      <li><a href="prml.preprocess.html#module-prml.preprocess.polynomial">prml.preprocess.polynomial (module)</a>
</li>
      <li><a href="prml.preprocess.html#module-prml.preprocess.sigmoidal">prml.preprocess.sigmoidal (module)</a>
</li>
      <li><a href="prml.rv.html#module-prml.rv">prml.rv (module)</a>
</li>
      <li><a href="prml.rv.html#module-prml.rv.bernoulli">prml.rv.bernoulli (module)</a>
</li>
      <li><a href="prml.rv.html#module-prml.rv.bernoulli_mixture">prml.rv.bernoulli_mixture (module)</a>
</li>
      <li><a href="prml.rv.html#module-prml.rv.beta">prml.rv.beta (module)</a>
</li>
      <li><a href="prml.rv.html#module-prml.rv.categorical">prml.rv.categorical (module)</a>
</li>
      <li><a href="prml.rv.html#module-prml.rv.dirichlet">prml.rv.dirichlet (module)</a>
</li>
      <li><a href="prml.rv.html#module-prml.rv.gamma">prml.rv.gamma (module)</a>
</li>
      <li><a href="prml.rv.html#module-prml.rv.gaussian">prml.rv.gaussian (module)</a>
</li>
      <li><a href="prml.rv.html#module-prml.rv.multivariate_gaussian">prml.rv.multivariate_gaussian (module)</a>
</li>
      <li><a href="prml.rv.html#module-prml.rv.multivariate_gaussian_mixture">prml.rv.multivariate_gaussian_mixture (module)</a>
</li>
      <li><a href="prml.rv.html#module-prml.rv.rv">prml.rv.rv (module)</a>
</li>
      <li><a href="prml.rv.html#module-prml.rv.students_t">prml.rv.students_t (module)</a>
</li>
      <li><a href="prml.rv.html#module-prml.rv.uniform">prml.rv.uniform (module)</a>
</li>
      <li><a href="prml.rv.html#module-prml.rv.variational_gaussian_mixture">prml.rv.variational_gaussian_mixture (module)</a>
</li>
      <li><a href="prml.sampling.html#module-prml.sampling">prml.sampling (module)</a>
</li>
      <li><a href="prml.sampling.html#module-prml.sampling.metropolis">prml.sampling.metropolis (module)</a>
</li>
      <li><a href="prml.sampling.html#module-prml.sampling.metropolis_hastings">prml.sampling.metropolis_hastings (module)</a>
</li>
      <li><a href="prml.sampling.html#module-prml.sampling.rejection_sampling">prml.sampling.rejection_sampling (module)</a>
</li>
      <li><a href="prml.sampling.html#module-prml.sampling.sir">prml.sampling.sir (module)</a>
</li>
      <li><a href="prml.bayesnet.html#prml.bayesnet.discrete.DiscreteVariable.proba">proba (prml.bayesnet.discrete.DiscreteVariable attribute)</a>

      <ul>
        <li><a href="prml.bayesnet.html#prml.bayesnet.DiscreteVariable.proba">(prml.bayesnet.DiscreteVariable attribute)</a>
</li>
      </ul></li>
      <li><a href="prml.dimreduction.html#prml.dimreduction.PCA.proba">proba() (prml.dimreduction.PCA method)</a>

      <ul>
        <li><a href="prml.dimreduction.html#prml.dimreduction.pca.PCA.proba">(prml.dimreduction.pca.PCA method)</a>
</li>
        <li><a href="prml.linear.html#prml.linear.BayesianLogisticRegression.proba">(prml.linear.BayesianLogisticRegression method)</a>
</li>
        <li><a href="prml.linear.html#prml.linear.LogisticRegression.proba">(prml.linear.LogisticRegression method)</a>
</li>
        <li><a href="prml.linear.html#prml.linear.SoftmaxRegression.proba">(prml.linear.SoftmaxRegression method)</a>
</li>
        <li><a href="prml.linear.html#prml.linear.VariationalLogisticRegression.proba">(prml.linear.VariationalLogisticRegression method)</a>
</li>
        <li><a href="prml.linear.html#prml.linear.bayesian_logistic_regression.BayesianLogisticRegression.proba">(prml.linear.bayesian_logistic_regression.BayesianLogisticRegression method)</a>
</li>
        <li><a href="prml.linear.html#prml.linear.logistic_regression.LogisticRegression.proba">(prml.linear.logistic_regression.LogisticRegression method)</a>
</li>
        <li><a href="prml.linear.html#prml.linear.softmax_regression.SoftmaxRegression.proba">(prml.linear.softmax_regression.SoftmaxRegression method)</a>
</li>
        <li><a href="prml.linear.html#prml.linear.variational_logistic_regression.VariationalLogisticRegression.proba">(prml.linear.variational_logistic_regression.VariationalLogisticRegression method)</a>
</li>
      </ul></li>
      <li><a href="prml.bayesnet.html#prml.bayesnet.probability_function.ProbabilityFunction">ProbabilityFunction (class in prml.bayesnet.probability_function)</a>
</li>
      <li><a href="prml.nn.math.html#prml.nn.math.product.prod">prod() (in module prml.nn.math.product)</a>

      <ul>
        <li><a href="prml.nn.array.html#prml.nn.array.array.Array.prod">(prml.nn.array.array.Array method)</a>
</li>
      </ul></li>
      <li><a href="prml.nn.math.html#prml.nn.math.product.Product">Product (class in prml.nn.math.product)</a>
</li>
  </ul></td>
</tr></table>

<h2 id="R">R</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="prml.bayesnet.html#prml.bayesnet.random_variable.RandomVariable">RandomVariable (class in prml.bayesnet.random_variable)</a>

      <ul>
        <li><a href="prml.nn.random.html#prml.nn.random.random.RandomVariable">(class in prml.nn.random.random)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.rv.RandomVariable">(class in prml.rv.rv)</a>
</li>
      </ul></li>
      <li><a href="prml.kernel.html#prml.kernel.RBF">RBF (class in prml.kernel)</a>

      <ul>
        <li><a href="prml.kernel.html#prml.kernel.rbf.RBF">(class in prml.kernel.rbf)</a>
</li>
      </ul></li>
      <li><a href="prml.nn.math.html#prml.nn.math.divide.rdivide">rdivide() (in module prml.nn.math.divide)</a>
</li>
      <li><a href="prml.bayesnet.html#prml.bayesnet.discrete.DiscreteProbability.receive_message">receive_message() (prml.bayesnet.discrete.DiscreteProbability method)</a>

      <ul>
        <li><a href="prml.bayesnet.html#prml.bayesnet.DiscreteVariable.receive_message">(prml.bayesnet.DiscreteVariable method)</a>
</li>
        <li><a href="prml.bayesnet.html#prml.bayesnet.discrete.DiscreteVariable.receive_message">(prml.bayesnet.discrete.DiscreteVariable method)</a>
</li>
      </ul></li>
      <li><a href="prml.linear.html#prml.linear.regression.Regression">Regression (class in prml.linear.regression)</a>
</li>
      <li><a href="prml.sampling.html#prml.sampling.rejection_sampling">rejection_sampling() (in module prml.sampling)</a>

      <ul>
        <li><a href="prml.sampling.html#prml.sampling.rejection_sampling.rejection_sampling">(in module prml.sampling.rejection_sampling)</a>
</li>
      </ul></li>
      <li><a href="prml.kernel.html#prml.kernel.RelevanceVectorClassifier">RelevanceVectorClassifier (class in prml.kernel)</a>

      <ul>
        <li><a href="prml.kernel.html#prml.kernel.relevance_vector_classifier.RelevanceVectorClassifier">(class in prml.kernel.relevance_vector_classifier)</a>
</li>
      </ul></li>
      <li><a href="prml.kernel.html#prml.kernel.RelevanceVectorRegressor">RelevanceVectorRegressor (class in prml.kernel)</a>

      <ul>
        <li><a href="prml.kernel.html#prml.kernel.relevance_vector_regressor.RelevanceVectorRegressor">(class in prml.kernel.relevance_vector_regressor)</a>
</li>
      </ul></li>
  </ul></td>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="prml.nn.nonlinear.html#prml.nn.nonlinear.relu.ReLU">ReLU (class in prml.nn.nonlinear.relu)</a>
</li>
      <li><a href="prml.nn.nonlinear.html#prml.nn.nonlinear.relu.relu">relu() (in module prml.nn.nonlinear.relu)</a>
</li>
      <li><a href="prml.markov.html#prml.markov.Particle.resample">resample() (prml.markov.Particle method)</a>

      <ul>
        <li><a href="prml.markov.html#prml.markov.particle.Particle.resample">(prml.markov.particle.Particle method)</a>
</li>
      </ul></li>
      <li><a href="prml.nn.array.html#prml.nn.array.reshape.Reshape">Reshape (class in prml.nn.array.reshape)</a>
</li>
      <li><a href="prml.nn.array.html#prml.nn.array.reshape.reshape">reshape() (in module prml.nn.array.reshape)</a>

      <ul>
        <li><a href="prml.nn.array.html#prml.nn.array.array.Array.reshape">(prml.nn.array.array.Array method)</a>
</li>
      </ul></li>
      <li><a href="prml.nn.array.html#prml.nn.array.reshape.reshape_method">reshape_method() (in module prml.nn.array.reshape)</a>
</li>
      <li><a href="prml.linear.html#prml.linear.RidgeRegression">RidgeRegression (class in prml.linear)</a>

      <ul>
        <li><a href="prml.linear.html#prml.linear.ridge_regression.RidgeRegression">(class in prml.linear.ridge_regression)</a>
</li>
      </ul></li>
      <li><a href="prml.nn.math.html#prml.nn.math.matmul.rmatmul">rmatmul() (in module prml.nn.math.matmul)</a>
</li>
      <li><a href="prml.nn.optimizer.html#prml.nn.optimizer.rmsprop.RMSProp">RMSProp (class in prml.nn.optimizer.rmsprop)</a>
</li>
      <li><a href="prml.nn.math.html#prml.nn.math.power.rpower">rpower() (in module prml.nn.math.power)</a>
</li>
      <li><a href="prml.nn.math.html#prml.nn.math.subtract.rsubtract">rsubtract() (in module prml.nn.math.subtract)</a>
</li>
  </ul></td>
</tr></table>

<h2 id="S">S</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="prml.nn.io.html#prml.nn.io.io.save_object">save_object() (in module prml.nn.io.io)</a>
</li>
      <li><a href="prml.nn.io.html#prml.nn.io.io.save_parameter">save_parameter() (in module prml.nn.io.io)</a>
</li>
      <li><a href="prml.bayesnet.html#prml.bayesnet.discrete.DiscreteProbability.send_message">send_message() (prml.bayesnet.discrete.DiscreteProbability method)</a>

      <ul>
        <li><a href="prml.bayesnet.html#prml.bayesnet.DiscreteVariable.send_message">(prml.bayesnet.DiscreteVariable method)</a>
</li>
        <li><a href="prml.bayesnet.html#prml.bayesnet.discrete.DiscreteVariable.send_message">(prml.bayesnet.discrete.DiscreteVariable method)</a>
</li>
      </ul></li>
      <li><a href="prml.bayesnet.html#prml.bayesnet.discrete.DiscreteProbability.send_message_to">send_message_to() (prml.bayesnet.discrete.DiscreteProbability method)</a>
</li>
      <li><a href="prml.nn.html#prml.nn.network.Network.set_parameter">set_parameter() (prml.nn.network.Network method)</a>
</li>
      <li><a href="prml.nn.html#prml.nn.network.Network.setting_parameter">setting_parameter (prml.nn.network.Network attribute)</a>
</li>
      <li><a href="prml.nn.array.html#prml.nn.array.array.Array.shape">shape (prml.nn.array.array.Array attribute)</a>

      <ul>
        <li><a href="prml.rv.html#prml.rv.Bernoulli.shape">(prml.rv.Bernoulli attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.Beta.shape">(prml.rv.Beta attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.Categorical.shape">(prml.rv.Categorical attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.Dirichlet.shape">(prml.rv.Dirichlet attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.Gamma.shape">(prml.rv.Gamma attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.Gaussian.shape">(prml.rv.Gaussian attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.MultivariateGaussian.shape">(prml.rv.MultivariateGaussian attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.MultivariateGaussianMixture.shape">(prml.rv.MultivariateGaussianMixture attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.StudentsT.shape">(prml.rv.StudentsT attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.Uniform.shape">(prml.rv.Uniform attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.bernoulli.Bernoulli.shape">(prml.rv.bernoulli.Bernoulli attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.beta.Beta.shape">(prml.rv.beta.Beta attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.categorical.Categorical.shape">(prml.rv.categorical.Categorical attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.dirichlet.Dirichlet.shape">(prml.rv.dirichlet.Dirichlet attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.gamma.Gamma.shape">(prml.rv.gamma.Gamma attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.gaussian.Gaussian.shape">(prml.rv.gaussian.Gaussian attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.multivariate_gaussian.MultivariateGaussian.shape">(prml.rv.multivariate_gaussian.MultivariateGaussian attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.multivariate_gaussian_mixture.MultivariateGaussianMixture.shape">(prml.rv.multivariate_gaussian_mixture.MultivariateGaussianMixture attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.students_t.StudentsT.shape">(prml.rv.students_t.StudentsT attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.uniform.Uniform.shape">(prml.rv.uniform.Uniform attribute)</a>
</li>
      </ul></li>
      <li><a href="prml.nn.nonlinear.html#prml.nn.nonlinear.sigmoid.Sigmoid">Sigmoid (class in prml.nn.nonlinear.sigmoid)</a>
</li>
      <li><a href="prml.nn.nonlinear.html#prml.nn.nonlinear.sigmoid.sigmoid">sigmoid() (in module prml.nn.nonlinear.sigmoid)</a>
</li>
      <li><a href="prml.nn.loss.html#prml.nn.loss.sigmoid_cross_entropy.sigmoid_cross_entropy">sigmoid_cross_entropy() (in module prml.nn.loss.sigmoid_cross_entropy)</a>
</li>
      <li><a href="prml.preprocess.html#prml.preprocess.SigmoidalFeature">SigmoidalFeature (class in prml.preprocess)</a>

      <ul>
        <li><a href="prml.preprocess.html#prml.preprocess.sigmoidal.SigmoidalFeature">(class in prml.preprocess.sigmoidal)</a>
</li>
      </ul></li>
      <li><a href="prml.nn.loss.html#prml.nn.loss.sigmoid_cross_entropy.SigmoidCrossEntropy">SigmoidCrossEntropy (class in prml.nn.loss.sigmoid_cross_entropy)</a>
</li>
      <li><a href="prml.sampling.html#prml.sampling.sir">sir() (in module prml.sampling)</a>

      <ul>
        <li><a href="prml.sampling.html#prml.sampling.sir.sir">(in module prml.sampling.sir)</a>
</li>
      </ul></li>
      <li><a href="prml.nn.array.html#prml.nn.array.array.Array.size">size (prml.nn.array.array.Array attribute)</a>

      <ul>
        <li><a href="prml.rv.html#prml.rv.Bernoulli.size">(prml.rv.Bernoulli attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.Beta.size">(prml.rv.Beta attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.Categorical.size">(prml.rv.Categorical attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.Dirichlet.size">(prml.rv.Dirichlet attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.Gamma.size">(prml.rv.Gamma attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.Gaussian.size">(prml.rv.Gaussian attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.MultivariateGaussian.size">(prml.rv.MultivariateGaussian attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.StudentsT.size">(prml.rv.StudentsT attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.Uniform.size">(prml.rv.Uniform attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.bernoulli.Bernoulli.size">(prml.rv.bernoulli.Bernoulli attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.beta.Beta.size">(prml.rv.beta.Beta attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.categorical.Categorical.size">(prml.rv.categorical.Categorical attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.dirichlet.Dirichlet.size">(prml.rv.dirichlet.Dirichlet attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.gamma.Gamma.size">(prml.rv.gamma.Gamma attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.gaussian.Gaussian.size">(prml.rv.gaussian.Gaussian attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.multivariate_gaussian.MultivariateGaussian.size">(prml.rv.multivariate_gaussian.MultivariateGaussian attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.students_t.StudentsT.size">(prml.rv.students_t.StudentsT attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.uniform.Uniform.size">(prml.rv.uniform.Uniform attribute)</a>
</li>
      </ul></li>
  </ul></td>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="prml.markov.html#prml.markov.Kalman.smooth">smooth() (prml.markov.Kalman method)</a>

      <ul>
        <li><a href="prml.markov.html#prml.markov.Particle.smooth">(prml.markov.Particle method)</a>
</li>
        <li><a href="prml.markov.html#prml.markov.kalman.Kalman.smooth">(prml.markov.kalman.Kalman method)</a>
</li>
        <li><a href="prml.markov.html#prml.markov.particle.Particle.smooth">(prml.markov.particle.Particle method)</a>
</li>
      </ul></li>
      <li><a href="prml.markov.html#prml.markov.Kalman.smoothing">smoothing() (prml.markov.Kalman method)</a>

      <ul>
        <li><a href="prml.markov.html#prml.markov.Particle.smoothing">(prml.markov.Particle method)</a>
</li>
        <li><a href="prml.markov.html#prml.markov.kalman.Kalman.smoothing">(prml.markov.kalman.Kalman method)</a>
</li>
        <li><a href="prml.markov.html#prml.markov.particle.Particle.smoothing">(prml.markov.particle.Particle method)</a>
</li>
      </ul></li>
      <li><a href="prml.nn.nonlinear.html#prml.nn.nonlinear.softmax.Softmax">Softmax (class in prml.nn.nonlinear.softmax)</a>
</li>
      <li><a href="prml.nn.nonlinear.html#prml.nn.nonlinear.softmax.softmax">softmax() (in module prml.nn.nonlinear.softmax)</a>
</li>
      <li><a href="prml.nn.loss.html#prml.nn.loss.softmax_cross_entropy.softmax_cross_entropy">softmax_cross_entropy() (in module prml.nn.loss.softmax_cross_entropy)</a>
</li>
      <li><a href="prml.nn.loss.html#prml.nn.loss.softmax_cross_entropy.SoftmaxCrossEntropy">SoftmaxCrossEntropy (class in prml.nn.loss.softmax_cross_entropy)</a>
</li>
      <li><a href="prml.linear.html#prml.linear.SoftmaxRegression">SoftmaxRegression (class in prml.linear)</a>

      <ul>
        <li><a href="prml.linear.html#prml.linear.softmax_regression.SoftmaxRegression">(class in prml.linear.softmax_regression)</a>
</li>
      </ul></li>
      <li><a href="prml.nn.nonlinear.html#prml.nn.nonlinear.softplus.Softplus">Softplus (class in prml.nn.nonlinear.softplus)</a>
</li>
      <li><a href="prml.nn.nonlinear.html#prml.nn.nonlinear.softplus.softplus">softplus() (in module prml.nn.nonlinear.softplus)</a>
</li>
      <li><a href="prml.nn.math.html#prml.nn.math.sqrt.Sqrt">Sqrt (class in prml.nn.math.sqrt)</a>
</li>
      <li><a href="prml.nn.math.html#prml.nn.math.sqrt.sqrt">sqrt() (in module prml.nn.math.sqrt)</a>
</li>
      <li><a href="prml.nn.math.html#prml.nn.math.square.Square">Square (class in prml.nn.math.square)</a>
</li>
      <li><a href="prml.nn.math.html#prml.nn.math.square.square">square() (in module prml.nn.math.square)</a>
</li>
      <li><a href="prml.markov.html#prml.markov.state_space_model.StateSpaceModel">StateSpaceModel (class in prml.markov.state_space_model)</a>
</li>
      <li><a href="prml.rv.html#prml.rv.variational_gaussian_mixture.VariationalGaussianMixture.student_t">student_t() (prml.rv.variational_gaussian_mixture.VariationalGaussianMixture method)</a>

      <ul>
        <li><a href="prml.rv.html#prml.rv.VariationalGaussianMixture.student_t">(prml.rv.VariationalGaussianMixture method)</a>
</li>
      </ul></li>
      <li><a href="prml.rv.html#prml.rv.StudentsT">StudentsT (class in prml.rv)</a>

      <ul>
        <li><a href="prml.rv.html#prml.rv.students_t.StudentsT">(class in prml.rv.students_t)</a>
</li>
      </ul></li>
      <li><a href="prml.nn.math.html#prml.nn.math.subtract.Subtract">Subtract (class in prml.nn.math.subtract)</a>
</li>
      <li><a href="prml.nn.math.html#prml.nn.math.subtract.subtract">subtract() (in module prml.nn.math.subtract)</a>
</li>
      <li><a href="prml.nn.math.html#prml.nn.math.sum.sum">sum() (in module prml.nn.math.sum)</a>

      <ul>
        <li><a href="prml.nn.array.html#prml.nn.array.array.Array.sum">(prml.nn.array.array.Array method)</a>
</li>
      </ul></li>
      <li><a href="prml.nn.math.html#prml.nn.math.sum.SumAxisOrKeepdims">SumAxisOrKeepdims (class in prml.nn.math.sum)</a>
</li>
      <li><a href="prml.bayesnet.html#prml.bayesnet.discrete.DiscreteVariable.summarize_message">summarize_message() (prml.bayesnet.discrete.DiscreteVariable method)</a>

      <ul>
        <li><a href="prml.bayesnet.html#prml.bayesnet.DiscreteVariable.summarize_message">(prml.bayesnet.DiscreteVariable method)</a>
</li>
      </ul></li>
      <li><a href="prml.nn.math.html#prml.nn.math.sum.SumSimple">SumSimple (class in prml.nn.math.sum)</a>
</li>
      <li><a href="prml.kernel.html#prml.kernel.SupportVectorClassifier">SupportVectorClassifier (class in prml.kernel)</a>

      <ul>
        <li><a href="prml.kernel.html#prml.kernel.support_vector_classifier.SupportVectorClassifier">(class in prml.kernel.support_vector_classifier)</a>
</li>
      </ul></li>
      <li><a href="prml.nn.array.html#prml.nn.array.array.Array.swapaxes">swapaxes() (prml.nn.array.array.Array method)</a>
</li>
  </ul></td>
</tr></table>

<h2 id="T">T</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="prml.kernel.html#prml.kernel.relevance_vector_classifier.RelevanceVectorClassifier.t">t (prml.kernel.relevance_vector_classifier.RelevanceVectorClassifier attribute)</a>

      <ul>
        <li><a href="prml.kernel.html#prml.kernel.RelevanceVectorClassifier.t">(prml.kernel.RelevanceVectorClassifier attribute)</a>
</li>
        <li><a href="prml.kernel.html#prml.kernel.RelevanceVectorRegressor.t">(prml.kernel.RelevanceVectorRegressor attribute)</a>
</li>
        <li><a href="prml.kernel.html#prml.kernel.relevance_vector_regressor.RelevanceVectorRegressor.t">(prml.kernel.relevance_vector_regressor.RelevanceVectorRegressor attribute)</a>
</li>
      </ul></li>
      <li><a href="prml.nn.nonlinear.html#prml.nn.nonlinear.tanh.Tanh">Tanh (class in prml.nn.nonlinear.tanh)</a>
</li>
      <li><a href="prml.nn.nonlinear.html#prml.nn.nonlinear.tanh.tanh">tanh() (in module prml.nn.nonlinear.tanh)</a>
</li>
      <li><a href="prml.rv.html#prml.rv.Gaussian.tau">tau (prml.rv.Gaussian attribute)</a>

      <ul>
        <li><a href="prml.rv.html#prml.rv.MultivariateGaussian.tau">(prml.rv.MultivariateGaussian attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.MultivariateGaussianMixture.tau">(prml.rv.MultivariateGaussianMixture attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.StudentsT.tau">(prml.rv.StudentsT attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.gaussian.Gaussian.tau">(prml.rv.gaussian.Gaussian attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.multivariate_gaussian.MultivariateGaussian.tau">(prml.rv.multivariate_gaussian.MultivariateGaussian attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.multivariate_gaussian_mixture.MultivariateGaussianMixture.tau">(prml.rv.multivariate_gaussian_mixture.MultivariateGaussianMixture attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.students_t.StudentsT.tau">(prml.rv.students_t.StudentsT attribute)</a>
</li>
      </ul></li>
  </ul></td>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="prml.dimreduction.html#prml.dimreduction.Autoencoder.transform">transform() (prml.dimreduction.Autoencoder method)</a>

      <ul>
        <li><a href="prml.dimreduction.html#prml.dimreduction.PCA.transform">(prml.dimreduction.PCA method)</a>
</li>
        <li><a href="prml.dimreduction.html#prml.dimreduction.autoencoder.Autoencoder.transform">(prml.dimreduction.autoencoder.Autoencoder method)</a>
</li>
        <li><a href="prml.dimreduction.html#prml.dimreduction.pca.PCA.transform">(prml.dimreduction.pca.PCA method)</a>
</li>
        <li><a href="prml.linear.html#prml.linear.FishersLinearDiscriminant.transform">(prml.linear.FishersLinearDiscriminant method)</a>
</li>
        <li><a href="prml.linear.html#prml.linear.fishers_linear_discriminant.FishersLinearDiscriminant.transform">(prml.linear.fishers_linear_discriminant.FishersLinearDiscriminant method)</a>
</li>
        <li><a href="prml.preprocess.html#prml.preprocess.GaussianFeature.transform">(prml.preprocess.GaussianFeature method)</a>
</li>
        <li><a href="prml.preprocess.html#prml.preprocess.PolynomialFeature.transform">(prml.preprocess.PolynomialFeature method)</a>
</li>
        <li><a href="prml.preprocess.html#prml.preprocess.SigmoidalFeature.transform">(prml.preprocess.SigmoidalFeature method)</a>
</li>
        <li><a href="prml.preprocess.html#prml.preprocess.gaussian.GaussianFeature.transform">(prml.preprocess.gaussian.GaussianFeature method)</a>
</li>
        <li><a href="prml.preprocess.html#prml.preprocess.polynomial.PolynomialFeature.transform">(prml.preprocess.polynomial.PolynomialFeature method)</a>
</li>
        <li><a href="prml.preprocess.html#prml.preprocess.sigmoidal.SigmoidalFeature.transform">(prml.preprocess.sigmoidal.SigmoidalFeature method)</a>
</li>
      </ul></li>
      <li><a href="prml.markov.html#prml.markov.Particle.transition_probability">transition_probability() (prml.markov.Particle method)</a>

      <ul>
        <li><a href="prml.markov.html#prml.markov.particle.Particle.transition_probability">(prml.markov.particle.Particle method)</a>
</li>
      </ul></li>
      <li><a href="prml.nn.random.html#prml.nn.random.normal.truncnormal">truncnormal() (in module prml.nn.random.normal)</a>
</li>
  </ul></td>
</tr></table>

<h2 id="U">U</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="prml.rv.html#prml.rv.Uniform">Uniform (class in prml.rv)</a>

      <ul>
        <li><a href="prml.rv.html#prml.rv.uniform.Uniform">(class in prml.rv.uniform)</a>
</li>
      </ul></li>
      <li><a href="prml.nn.random.html#prml.nn.random.uniform.uniform">uniform() (in module prml.nn.random.uniform)</a>
</li>
      <li><a href="prml.nn.optimizer.html#prml.nn.optimizer.ada_delta.AdaDelta.update">update() (prml.nn.optimizer.ada_delta.AdaDelta method)</a>

      <ul>
        <li><a href="prml.nn.optimizer.html#prml.nn.optimizer.ada_grad.AdaGrad.update">(prml.nn.optimizer.ada_grad.AdaGrad method)</a>
</li>
        <li><a href="prml.nn.optimizer.html#prml.nn.optimizer.adam.Adam.update">(prml.nn.optimizer.adam.Adam method)</a>
</li>
        <li><a href="prml.nn.optimizer.html#prml.nn.optimizer.gradient.Gradient.update">(prml.nn.optimizer.gradient.Gradient method)</a>
</li>
        <li><a href="prml.nn.optimizer.html#prml.nn.optimizer.momentum.Momentum.update">(prml.nn.optimizer.momentum.Momentum method)</a>
</li>
        <li><a href="prml.nn.optimizer.html#prml.nn.optimizer.optimizer.Optimizer.update">(prml.nn.optimizer.optimizer.Optimizer method)</a>
</li>
        <li><a href="prml.nn.optimizer.html#prml.nn.optimizer.rmsprop.RMSProp.update">(prml.nn.optimizer.rmsprop.RMSProp method)</a>
</li>
      </ul></li>
  </ul></td>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="prml.nn.array.html#prml.nn.array.array.Array.update_grad">update_grad() (prml.nn.array.array.Array method)</a>
</li>
      <li><a href="prml.markov.html#prml.markov.Kalman.update_parameter">update_parameter() (prml.markov.Kalman method)</a>

      <ul>
        <li><a href="prml.markov.html#prml.markov.kalman.Kalman.update_parameter">(prml.markov.kalman.Kalman method)</a>
</li>
      </ul></li>
      <li><a href="prml.kernel.html#prml.kernel.RBF.update_parameters">update_parameters() (prml.kernel.RBF method)</a>

      <ul>
        <li><a href="prml.kernel.html#prml.kernel.rbf.RBF.update_parameters">(prml.kernel.rbf.RBF method)</a>
</li>
      </ul></li>
  </ul></td>
</tr></table>

<h2 id="V">V</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="prml.dimreduction.html#prml.dimreduction.PCA.var">var (prml.dimreduction.PCA attribute)</a>

      <ul>
        <li><a href="prml.dimreduction.html#prml.dimreduction.pca.PCA.var">(prml.dimreduction.pca.PCA attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.Gaussian.var">(prml.rv.Gaussian attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.gaussian.Gaussian.var">(prml.rv.gaussian.Gaussian attribute)</a>
</li>
      </ul></li>
      <li><a href="prml.rv.html#prml.rv.VariationalGaussianMixture">VariationalGaussianMixture (class in prml.rv)</a>

      <ul>
        <li><a href="prml.rv.html#prml.rv.variational_gaussian_mixture.VariationalGaussianMixture">(class in prml.rv.variational_gaussian_mixture)</a>
</li>
      </ul></li>
  </ul></td>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="prml.linear.html#prml.linear.VariationalLinearRegression">VariationalLinearRegression (class in prml.linear)</a>

      <ul>
        <li><a href="prml.linear.html#prml.linear.variational_linear_regression.VariationalLinearRegression">(class in prml.linear.variational_linear_regression)</a>
</li>
      </ul></li>
      <li><a href="prml.linear.html#prml.linear.VariationalLogisticRegression">VariationalLogisticRegression (class in prml.linear)</a>

      <ul>
        <li><a href="prml.linear.html#prml.linear.variational_logistic_regression.VariationalLogisticRegression">(class in prml.linear.variational_logistic_regression)</a>
</li>
      </ul></li>
      <li><a href="prml.markov.html#prml.markov.hmm.HiddenMarkovModel.viterbi">viterbi() (prml.markov.hmm.HiddenMarkovModel method)</a>
</li>
  </ul></td>
</tr></table>

<h2 id="W">W</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="prml.dimreduction.html#prml.dimreduction.PCA.W">W (prml.dimreduction.PCA attribute)</a>

      <ul>
        <li><a href="prml.dimreduction.html#prml.dimreduction.pca.PCA.W">(prml.dimreduction.pca.PCA attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.VariationalGaussianMixture.W">(prml.rv.VariationalGaussianMixture attribute)</a>
</li>
        <li><a href="prml.rv.html#prml.rv.variational_gaussian_mixture.VariationalGaussianMixture.W">(prml.rv.variational_gaussian_mixture.VariationalGaussianMixture attribute)</a>
</li>
      </ul></li>
      <li><a href="prml.linear.html#prml.linear.variational_linear_regression.VariationalLinearRegression.w_mean">w_mean (prml.linear.variational_linear_regression.VariationalLinearRegression attribute)</a>

      <ul>
        <li><a href="prml.linear.html#prml.linear.VariationalLinearRegression.w_mean">(prml.linear.VariationalLinearRegression attribute)</a>
</li>
      </ul></li>
  </ul></td>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="prml.linear.html#prml.linear.variational_linear_regression.VariationalLinearRegression.w_var">w_var (prml.linear.variational_linear_regression.VariationalLinearRegression attribute)</a>

      <ul>
        <li><a href="prml.linear.html#prml.linear.VariationalLinearRegression.w_var">(prml.linear.VariationalLinearRegression attribute)</a>
</li>
      </ul></li>
      <li><a href="prml.markov.html#prml.markov.Particle.weigh">weigh() (prml.markov.Particle method)</a>

      <ul>
        <li><a href="prml.markov.html#prml.markov.particle.Particle.weigh">(prml.markov.particle.Particle method)</a>
</li>
      </ul></li>
  </ul></td>
</tr></table>

<h2 id="X">X</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="prml.kernel.html#prml.kernel.relevance_vector_classifier.RelevanceVectorClassifier.X">X (prml.kernel.relevance_vector_classifier.RelevanceVectorClassifier attribute)</a>

      <ul>
        <li><a href="prml.kernel.html#prml.kernel.RelevanceVectorClassifier.X">(prml.kernel.RelevanceVectorClassifier attribute)</a>
</li>
        <li><a href="prml.kernel.html#prml.kernel.RelevanceVectorRegressor.X">(prml.kernel.RelevanceVectorRegressor attribute)</a>
</li>
        <li><a href="prml.kernel.html#prml.kernel.relevance_vector_regressor.RelevanceVectorRegressor.X">(prml.kernel.relevance_vector_regressor.RelevanceVectorRegressor attribute)</a>
</li>
      </ul></li>
  </ul></td>
</tr></table>

<h2 id="Z">Z</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
  <td style="width: 33%; vertical-align: top;"><ul>
      <li><a href="prml.nn.array.html#prml.nn.array.zeros.zeros">zeros() (in module prml.nn.array.zeros)</a>
</li>
  </ul></td>
</tr></table>



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